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Evaluation of engineering properties of soil for sustainable urban development

  • Saleh Qaysi EMAIL logo , Awad Al-Shmrani and Kamal Abdelrahman
Published/Copyright: May 28, 2025
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Open Geosciences
From the journal Open Geosciences Volume 17 Issue 1

Abstract

Rapid urbanization in southern Saudi Arabia necessitates accurate geotechnical assessments to support sustainable development. However, the region lacks comprehensive seismic-based soil classification studies, raising concerns regarding construction safety. This study utilizes seismic refraction and tomography techniques to characterize soil properties and evaluate their appropriateness for infrastructure development. Ten seismic refraction and tomography surveys and two multichannel analysis of surface wave profiles were conducted to classify soil conditions based on seismic velocities and geotechnical parameters. The results reveal three distinct subsurface layers: (1) alluvial soil (V p = 300–940 m/s), (2) fractured basement (V p = 1,350–1,890 m/s), and (3) massive basement (V p > 2,400 m/s). According to National Earthquake Hazards Reduction Program criteria, soil classification identifies loose sediments (Class E) as unsuitable for construction, while stiffer deposits (Classes D and C) offer stable foundations. Integrating seismic techniques presents a cost-effective alternative to traditional drilling, providing a rapid assessment tool for urban planners. These findings aid in optimizing construction site selection, thereby minimizing risks associated with weak soil zones.

Keywords: geotechnical properties; sustainable development; Saudi Arabia

1 Introduction

Seismic refraction is a geophysical method that has gained increasing importance for detailed mapping of near-surface conditions, particularly in site investigations for civil and geotechnical engineering projects. This method measures the refraction of seismic waves as they pass through various subsurface layers, offering essential insights into the depth and characteristics of these layers [1]. When used with exploratory drilling, seismic refraction becomes a highly effective tool for shallow surveys, allowing engineers and geologists to completely understand subsurface conditions and make well-informed decisions regarding site development and construction [2,3,4]. The conventional interpretation of seismic refraction data depicts the subsurface as a series of discrete, layered media, with each layer defined by its unique seismic velocity. In this approach, the seismic waves’ travel times, as they return to the surface, are used to calculate the depth and properties of each subsurface layer. By processing these travel times, which indicate the speed at which seismic waves move through each layer, geophysicists can transform the data into depth estimates and generate a detailed profile of the subsurface structure [5].

Nowadays, advancements in interpretation techniques allow for analyzing velocity changes as discrete layers and gradients. This method, referred to as refraction tomography, utilizes ray tracing algorithms to accommodate gradual variations in velocity. It offers a detailed interpretation of the travel paths of first-arrival seismic waves as they traverse the subsurface. This approach facilitates a more nuanced understanding of subsurface structures by capturing subtle and incremental changes in seismic velocity [6]. By utilizing more data points and advanced processing techniques, seismic refraction tomography (SRT) generates a two-dimensional (2D) model that reveals finer details of the subsurface, enabling a more precise characterization of the interfaces between different layers, including complex geological features such as fractures or material properties. This enhanced precision makes SRT particularly valuable for projects requiring detailed subsurface characterization and detecting complex geological structures that may not be captured with traditional methods. Several researchers have utilized seismic methods to map basement layers, engineering bedrock, and subsurface structures for infrastructural and civil engineering purposes [3,7,8,9,10,11,12,13,14,15,16]. In southern Saudi Arabia, few geophysical investigations have been conducted to estimate the bedrock depth and assess the geotechnical properties of soil deposits [2,4,17,18]. Moreover, recent advances in soil mechanics are pushing for a more sustainable approach to land use and construction [19,20,21]. Researchers focus on reducing the environmental impact of soil interventions, enhancing natural processes that benefit the soil, and contributing to long-term sustainability goals [22,23,24].

In this study, a comprehensive seismic refraction survey was conducted in the Ahad Rafidah area, Khamis Mushait region, to investigate and map the subsurface geological characteristics through ten seismic refraction lines, ten SRT profiles, and two additional multichannel analysis of surface wave (MASW) profiles close to each other in the study area. Each line was oriented uniquely to ensure comprehensive area coverage, allowing a more accurate interpretation of subsurface features. Together, these survey lines spanned a total length of 115 m. Along each line, geophones were strategically placed at intervals of 5 m, facilitating the collection of high-resolution seismic data for subsequent analysis. The primary objective of this extensive survey is to assess and map the soil profile in the study area. This included determining the soil layer’s thickness, composition, and seismic velocities. By mapping these layers, the study aimed to offer a clearer understanding of their properties, which are crucial for assessing the geotechnical parameters of the soil. These factors are vital for determining the soil’s suitability for urban and sustainable construction and understanding how they may impact the stability and safety of future structures on the site. The shear wave velocity of near-surface materials such as soil and rock and their influence on seismic wave propagation are fundamental topics in engineering and environmental studies. This area has gained significant attention from the Ministry of Housing due to the rapid urban expansion on its borders. Moreover, the study area has a flat and low relief altitude and has not been previously studied from geological, geophysical, and geotechnical engineering perspectives. A few geotechnical studies have been carried out in southern Saudi Arabia. It is crucial to assess the foundation layer depth and the engineering properties of the soil, as well as evaluate its capacity to withstand the stresses and loads generated by construction activities. A thorough understanding of the soil’s strength, stability, and behavior under pressure is essential to ensure the structural integrity of buildings or infrastructure that will be established in this area. The aim of this study is to apply various geophysical techniques and assess the soil conditions to provide new insights into soil stabilization, urban planning, and resource conservation while aligning with global sustainability goals.

The study area is located in the Ahad Rafidah district, south of Saudi Arabia, roughly 18 km northwest of Khamis Mushait city. It is bordered by longitudes 42.855860°E and 42.870948°E and latitudes 18.217938°N and 18.206971°N (Figure 1).

Figure 1 Map showing the location of the study area and the seismic refraction profiles within it.
Figure 1

Map showing the location of the study area and the seismic refraction profiles within it.

2 Geological setting

Geologically, most of the study area is situated on Khamis Mushait Gneiss, which is topped by a considerable layer of alluvial sedimentary soil. The Khamis Mushait Gneiss serves as the primary basement rock unit in the region, with its exposure occurring at the surface along the northwestern, southern, and southeastern boundaries of the investigated site. This gneissic bedrock is crucial for understanding the geological framework of the area, while the overlying alluvial sediments add complexity to the subsurface conditions (Figure 2).

Figure 2 Geologic map of the study area.
Figure 2

Geologic map of the study area.

Upper Proterozoic geological formations in the area comprise metamorphosed volcanic and sedimentary rocks from the Baish, Baha, Jiddah, and Halaban groups, in addition to upper Proterozoic plutonic rocks with compositions varying from gabbro to granite. Tertiary and Quaternary basalt, along with Quaternary surficial deposits, are found overlying the Proterozoic rock formations in the region (Figure 2). Although the rock formations in the area demonstrate overall structural stability, a significant network of joints and fractures is present. The biotite granodiorite and monzogranite rocks exhibit considerable foliation and were previously classified as Khamis Mushait gneiss in earlier studies [25,26]. These joints and fractures could act as conduits for the migration of leachates, such as pollution plumes, facilitating their interaction with the surrounding environment. The basement gneiss rocks are buried beneath a substantial layer of alluvial soil, which consists of a mixture of pebbles, gravel, sands, and clays. This alluvial soil has been deposited at the site through flowing water that has carried materials from upstream sources and the surrounding weathered gneiss hills. The sediment has been transported via a network of narrow, active channels carved through the landscape, contributing to the accumulation of these sediments at the investigated location [27].

3 Methodology

In a seismic refraction survey, elastic waves are artificially produced using either an explosive source or a hammer. Due to the rock’s elastic properties, the resulting deformation spreads in spherical wavefronts in all directions [28]. During critical refraction, refracted waves travel along the interface and generate secondary waves that return to the surface. These returning waves are captured during the seismic refraction survey. Typically, a seismic profile includes 24 vertical geophones spaced evenly 5 m apart. Waves are generated using a 10 kg sledgehammer as the source of seismic energy, and a metal base plate is used to ensure consistent energy transmission from the hammer. Depending on the topography and layout of the profile, five to seven shots are usually employed to record the waves. P-wave velocities provide insights into the characteristics of the overlying soils, fracturing, and weathering within a rock mass rather than directly identifying the rock type itself. The penetration depth is influenced by the length of the surface profile, which encompasses geophones and source points, as well as the expected subsurface velocities.

The shear wave velocity was determined from the primary wave velocity using the following equation:

(1) V P ≈ 1.7 V s .

3.1 SRT

SRT survey aims to generate a detailed 2D P-wave velocity model of the subsurface. This model provides valuable insights into the distribution of seismic wave velocities, which helps identify variations in subsurface materials, such as changes in soil composition, density, and rock formations. By mapping P-wave velocities, the survey aids in understanding the geotechnical properties and structural integrity of the subsurface layers, which is essential for various engineering and construction applications. In geotechnical engineering, higher P-wave velocities indicate denser and more stable subsurface materials, which are preferable for construction due to their superior load-bearing capacity and resistance to deformation. Lower P-wave velocities suggest softer, less consolidated soils, requiring additional engineering measures to ensure stability in construction projects [29]. Using the velocity model, it is possible to determine the thickness of the soil and weathered layer and the depth of the basement rock. This analysis relies on variations in seismic wave velocities, which indicate changes in material density and composition at various subsurface levels. SRT is an effective technique for evaluating the rippability of materials, owing to the significant contrast in P-wave velocities (V p) between deposits, weathered basement, and fresh basement rock. These variations in velocity offer essential insights into the subsurface layers [30,31,32]. SRT is a geophysical method that employs inversion techniques to generate a subsurface 2D velocity-depth profile. This method entails measuring the travel times of seismic waves and subsequently using an inversion algorithm to reconstruct the velocity distribution at different depths. The resulting 2D profile offers detailed insights into the varying seismic velocities within subsurface layers essential for detecting changes in material properties, including soil composition, density, and rock type. This technique is commonly employed in geotechnical investigations, aiding in the mapping of subsurface structures and evaluating their suitability for construction and other engineering applications [6,33,34] that images the cross-sectional image along the survey profile by analyzing the soil’s response to energy generated from an external source, such as a hammer impact. The energy propagates through the subsurface, and the resulting data help visualize the variation in material properties along the profile [35,36,37].

3.2 Geotechnical parameters

Geotechnical parameters (Table 1) represent soil’s essential physical and mechanical properties that are used to assess their behavior in engineering applications. These parameters are crucial for designing sustainable structures such as foundations, retaining walls, dams, and tunnels. They help engineers assess the strength, stability, compressibility, and overall performance under loading conditions.

Table 1

Geotechnical parameters used in the current study

Parameters Used equations References
Poisson’s ratio σ = 1 2 1 − 1 Vp Vs 2 − 1 [38]
Young’s modulus E = ρ ( 3 Vp 2 − 4 Vs 2 ) Vp Vs 2 − 1 [38]
Bulk modulus β = E 3 ( 1 − 2 σ ) [39]
Shear modulus µ = E ( 2 ( 1 + σ ) ) = ρ Vs 2 [39]
Concentration index C i = 3 − 4 Vs 2 Vp 2 1 − 2 Vs 2 Vp 2 [41]
Material index V = 3 − ( Vp / Vs ) 2 ( Vp / Vs ) 2 − 1 = ( 1 − 4 σ ) [40]
Stress ratio S i = 1 − 2 Vs 2 Vp 2 = ( C i − 2 ) − 1 [41]
Ultimate bearing capacity log Q ult = 2.932 ( log Vs − 1.45 ) [41]
Density ρ = 0.31 Vp 0.25 or ρ = 0.44 Vs 0.25 [42]

3.3 MASW method

MASW is a geophysical method that uses surface wave (Rayleigh wave) propagation to profile the subsurface characteristics based on shear wave velocity estimation. MASW method has wide-ranging engineering and geotechnical applications in understanding subsurface conditions and urban development. MASW was used to determine the depth to bedrock, shear strength or soil stability, liquefaction studies, sinkhole mapping, fault mapping, and earthquake resilience assessment. The most common application is the determination of the shear strength, in other words, the stiffness of the ground.

4 Data acquisition and field procedure

Seismic refraction surveys and seismic tomography were conducted in the study area along ten distinct profiles, as depicted in Figure 3, each spanning 115 m long and utilizing a 24-channel seismograph system. The seismic refraction method involved several essential components: a seismograph to record seismic signals, a 12V DC battery to power the equipment, a trigger cable to synchronize the seismic source with the recording system, two seismic cable reels with signal cables to connect the geophones, a 10 kg sledgehammer as the source of seismic energy, a metal base plate to ensure consistent energy transmission from the hammer, and 24 geophones, each with a frequency of 4.5 Hz for detecting the refracted seismic waves. In the field setup, the 24 geophones were evenly distributed along the survey line with 5 m geophone spacing and connected to the seismic cable reels, which were then linked to the seismograph for data acquisition. A 5 m geophone spacing balances resolution and practical field conditions, making it ideal for detecting medium-scale subsurface features like soil layers and significant lateral variations. For broader surveys focusing on general subsurface conditions, this 5 m spacing is usually adequate, providing a solid compromise between detail and operational efficiency. In the study area, lateral variations in depositional rates are absent due to its desert environment, unlike river deltas that experience high sedimentation rates. A 5 m geophone spacing may obscure these changes in areas with slight lateral variations.

Figure 3 Base map of the study area and distributions of seismic refraction and tomography.
Figure 3

Base map of the study area and distributions of seismic refraction and tomography.

Additionally, a 5 m spacing enhances signal quality. Using larger spacing improves the signal-to-noise (S/N) ratio, as seismic waves are less likely to scatter due to small heterogeneities. This results in more precise data for larger-scale features, such as the depth to bedrock or broad variations in soil stiffness. Moreover, field data collection occurred during quiet hours, from 12 AM to 6 AM on weekends, when traffic was minimal. The geophones were also buried in the soil to minimize noise interference. A 10 kg sledgehammer striking the metal base plate generated seismic energy, which is practical in environments with low ambient noise. Each seismic refraction profile utilized five shot points: two offset shots located beyond the ends of the profile, a forward shot near the start of the line, a midpoint shot at the center, and a reverse shot near the end of the line, as illustrated in Figure 4.

Figure 4 Seismic refraction survey field configuration.
Figure 4

Seismic refraction survey field configuration.

However, for SRT, the shot points are repeated at each geophone (i.e., 24 shot points per profile). This configuration allowed for comprehensive seismic data collection, capturing wave propagation from various distances and providing detailed information on the subsurface structure. Each shot point underwent multiple recordings with stacking processes to improve the S/N ratio. This technique involved repeating the seismic source activation and averaging the results to minimize random noise and enhance the clarity of the seismic signals. The high-quality data obtained through this process allowed for accurate interpretation of seismic wave travel times and refraction patterns, enabling the identification of subsurface geological layers, their depth, and material properties. The data provided valuable insights into the subsurface structure and were critical for detailed geophysical analysis.

The MASW method was also carried out on two profiles using 4.5 Hz vertical geophones. Each profile spans 60 m with a 1 m spacing between geophones, resulting in 24 geophones arranged in a 1D receiver array. The source was placed 10 m from the first geophone (Figure 5), with a sample interval of 0.125 ms, two stackings, and a recording duration of 0.512 s.

Figure 5 The geometry of the MASW field data acquisition.
Figure 5

The geometry of the MASW field data acquisition.

5 Data processing and results

The seismic refraction data collected in the field were processed and analyzed using the SeisImager software (Geometrics corporation) along with the Pickwin and Plotrefa programs. Pickwin is essential in the initial stages of the workflow, where it is used to identify and mark the first seismic arrivals, commonly known as first breaks. These first breaks are crucial as they represent the initial energy wave traveling through the subsurface and are saved for later analysis. Subsequently, Plotrefa is utilized to refine the processing of these data, aiding in further editing the identified first arrivals and assigning the corresponding geological layers based on the seismic wave velocities. Plotrefa also facilitates the calculation of P-wave velocities, which are crucial for assessing the material properties of the subsurface. This is achieved through advanced techniques such as inverse modeling, where the subsurface model is refined to align with the observed data, and iterative ray tracing, which enhances the model’s accuracy by tracing the paths of seismic waves (refer Figure 6 for seismic refraction and Figure 7 for SRT). Together, these software tools enable detailed processing and interpretation of the seismic refraction data, accurately characterizing the subsurface structure. This thorough analysis offers insights into the depth, composition, and properties of the geological layers beneath the surface.

Figure 6 Seismic refraction data processing sequence.
Figure 6

Seismic refraction data processing sequence.

Figure 7 SRT data processing sequence.
Figure 7

SRT data processing sequence.

MASW field-collected data are processed using SurfSeis software through a series of steps to generate V s profiles. These steps involve retrieving shot gather files from the field (Figure 8a), extracting the dispersion curve (Figure 8b), and inverting it to obtain the V s profile (Figure 8c). The creation of the dispersion curve is a key step, typically shown as a function of frequency, with phase velocity indicating how the S/N ratio changes with frequency. This iterative process is employed to determine the fundamental mode of the subsurface soil profile (Figure 8d). Low-frequency waves are utilized to probe deeper subsurface layers, as the wavelength is inversely related to frequency. Figure 8 illustrates a typical data processing setup.

Figure 8 MASW data processing sequence: (a) The raw data, (b) the dispersion curve showing the approximate phase velocity and reference frequency, (c) 1D shear wave velocity model, and (d) the resulted 2D shear wave velocity section.
Figure 8

MASW data processing sequence: (a) The raw data, (b) the dispersion curve showing the approximate phase velocity and reference frequency, (c) 1D shear wave velocity model, and (d) the resulted 2D shear wave velocity section.

Ten seismic refraction and seismic tomography profiles were carried out simultaneously, and two MASW profiles were added. Following the seismic data collection, it was processed, and the final model, generated using Plotrefa and SurfSeis software, was interpreted to identify three distinct subsurface layers based on P-wave and S-wave velocities. The first layer displayed P-wave velocities between 300 and 940 m/s, indicating an alluvium soil layer approximately 2 m thick near the top of the valley in the northeast, extending to a depth of 8 m toward the southwest of the study area. Profile No. 2, situated in the southwest region of the study area, exhibits the most significant soil thickness, as illustrated in Figure 9. The second layer, identified as a weathered basement, showed velocities ranging from 1,350 to 2,630 m/s. Its thickness varied considerably across different profiles, reaching up to 30 m in Profiles 3 and 4, as depicted in Figures 9 and 10. This variation in thickness is particularly notable in areas where the layer is disturbed, suggesting geological instability and providing strong evidence of groundwater presence, particularly in the eastern part of the study area. The variations in thickness may indicate the impact of hydrological factors, such as water saturation and erosion, on the integrity and composition of the weathered basement layer. The third layer, representing the fresh basement, was characterized by significantly higher velocities, ranging from 2,400 to 6,876 m/s. This pronounced increase in velocity suggests a more solid and cohesive geological formation compared to the layers above. The massive basement typically comprises less weathered rock material, suggesting greater density and structural integrity. Such characteristics imply that this layer is less influenced by surface processes like weathering and erosion, resulting in a stable foundation. The thickness of the alluvium soil layer within the study area exhibits significant spatial variability. At Profile No. 5, located in the southern area of the study area, the alluvium soil layer has an estimated thickness of approximately 4 m, as inferred from a P-wave velocity measurement of 530 m/s. In contrast, Profile No. 6, situated in the northwestern section of the study area, shows a slightly thinner soil layer with a thickness of about 3 m. This thickness was determined based on the P-wave velocity of 470 m/s measured at this location. These variations in thickness suggest differences in the composition and properties of the underlying soil in the study area, illustrated in Figure 10. At Profile No. 7, located in the northern region of the study area, the alluvial soil layer is estimated to have a thickness of around 3 m, as inferred from a P-wave velocity measurement of 400 m/s. Meanwhile, Profile No. 8, situated in the central part of the study area, shows a thicker alluvial soil layer, with an estimated thickness of 5 m, based on a P-wave velocity of 530 m/s. Additionally, Profile No. 9 exhibits a variable alluvial soil thickness ranging from 2 to 3 m, determined from a P-wave velocity of 300 m/s, as illustrated in Figure 11.

Figure 9 Velocity models of seismic refraction for Profiles 1, 2, and 3.
Figure 9

Velocity models of seismic refraction for Profiles 1, 2, and 3.

Figure 10 Velocity models of seismic refraction for Profiles 4, 5, and 6.
Figure 10

Velocity models of seismic refraction for Profiles 4, 5, and 6.

Figure 11 Velocity models of seismic refraction for Profiles 7, 8, and 9.
Figure 11

Velocity models of seismic refraction for Profiles 7, 8, and 9.

SRT uncovers variations in seismic velocities, facilitating the interpretation of subsurface lithology. This method generates a detailed model illustrating the subsurface layers, offering valuable insights into their composition and structure. Changes in seismic wave velocities help identify different geological structures while delineating boundaries at various depths. The resulting model provides a comprehensive understanding of the stratigraphy, aiding in recognizing key subsurface features. Figures 12–14 display velocity variations with depth, highlighting the geological structure across the tomography models. These figures demonstrate how seismic velocities differ across various layers, enabling a more accurate assessment of the subsurface geoseismic characteristics in the study area (Table 2).

Figure 12 Velocity models of SRT for Profiles 1, 2, and 3.
Figure 12

Velocity models of SRT for Profiles 1, 2, and 3.

Figure 13 Velocity models of SRT for Profiles 4, 5 and 6.
Figure 13

Velocity models of SRT for Profiles 4, 5 and 6.

Figure 14 Velocity models of SRT for Profiles 7, 8 and 9.
Figure 14

Velocity models of SRT for Profiles 7, 8 and 9.

Table 2

Results of geotechnical parameters for soil profile in the study area

Profile no: V p (m/s) V s (m/s) Density gm/cm3 (R 2 = 0.9) Poisson’s ratio (R 2 = 0.97) Shear modulus dyn/cm2 (R 2 = 0.93) Young’s modulus dyn/cm2 (R 2 = 0.96) Bulk modulus dyn/cm2 (R 2 = 0.89) Material index (R 2 = 0.902) Concentration index (R 2 = 0.943) Stress ratio SI (R 2 = 0.904) Ultimate bearing capacity kg/cm2 (R 2 = 0.883)
P1 590 347 1.52782882 0.235586826 1,839,643,398 4.5 × 109 2,862,648,728 0.057652696 5.244719524 0.308193048 1.573424905
P2 940 553 1.71649941 0.235363652 5,249,209,666 1.3 × 1010 8,159,874,480 0.058545391 5.248744402 0.307811227 6.169785776
P3 660 388 1.57125864 0.235939605 2,365,435,601 5.8 × 109 3,686,797,996 0.056241581 5.238372785 0.308797062 2.183004442
P4 520 306 1.4803436 0.235138377 1,386,134,531 3.4 × 109 2,152,515,044 0.059446494 5.252814936 0.307426036 1.088266405
P5 530 312 1.48740988 0.234838414 1,447,904,271 3.6 × 109 2,245,347,723 0.060646342 5.258247112 0.306913492 1.152023318
P6 470 276 1.4433982 0.236823194 1,099,523,010 2.7 × 109 1,720,713,500 0.052707222 5.222559374 0.310312359 0.804165484
P7 400 235 1.38636215 0.236459079 765618495.2 1.9 × 109 1,196,157,419 0.054163684 5.229061554 0.3096875 0.501846584
P8 530 312 1.48740988 0.234838414 1,447,904,271 3.6 × 109 2,245,347,723 0.060646342 5.258247112 0.306913492 1.152023318
P9 540 318 1.49437686 0.234549235 1,511,173,655 3.7 × 109 2,340,362,010 0.061803062 5.26349718 0.306419753 1.218193606
P10 300 176 1.29015535 0.237598265 399638521.1 9.9 × 108 627660164.7 0.04960694 5.20878494 0.311644444 0.215002318

MASW measures and analyzes surface wave velocities, offering valuable insights into subsurface properties such as shear wave velocity, stratigraphy, and soil composition. MASW profiles for Profiles No. 1 and 2 reveal three distinct layers based on shear wave velocity values. The first layer, with velocities ranging from 164 to 585 m/s, corresponds to alluvial soil. The second layer exhibits higher velocities, ranging from 586 to 1,300 m/s, indicative of a fractured basement. The third layer is characterized by even higher velocities, between 1,300 and 2,000 m/s, signifying a massive basement, as illustrated in Figure 15.

Figure 15 2D MASW geoseismic cross section of Profiles 1 and 2.
Figure 15

2D MASW geoseismic cross section of Profiles 1 and 2.

6 Discussion and conclusion

As indicated above, V p and V s increased with depth, aligning with expected compaction gradients as observed in previous geotechnical studies of the Arabian Shield. The significant increase in velocity with depth reflects the transition from fractured material to more consolidated and rigid rock mass [38,39,40,41,42]. The uppermost layer composed of sand and gravel (V p = 300–940 m/s and V s = 164–585 m/s); Second layer (V p = 1,350–1,890 m/s and V s = 586–1,300 m/s) indicate a more consolidated material, typically a fractured basement; third layer (V p = 2,430–6,880 m/s and V s = 1,300–2,000 m/s) is a massive basement rock.

Similar studies in the Arabian Shield region [2,4,17,18] report comparable velocity trends, but our study refines these estimates by incorporating MASW, providing a more detailed classification of soil stiffness.

The contour map in Figure 16a illustrates an increase in P-wave velocity from the northeast to the southwest in the study area, indicating a compaction gradient trend. The thickness of this surface layer varies from 2 to 8 m, showing a noticeable gradient of increasing thickness as one moves from the northeast toward the southwest of the study area, as depicted in Figure 16b. This variation in thickness may indicate changes in sediment deposition patterns, possibly influenced by historical geological processes or hydrological factors [43]. The sand and gravel in this layer suggest favorable drainage properties, which are important considerations for land use planning and environmental assessments [44]. The second layer is classified as a weathered basement, exhibiting P-wave velocities ranging from 1,350 to 2,630 m/s. This layer reflects various geological processes that have altered the rock, resulting in varying degrees of weathering and consolidation [45,46]. These processes can include chemical weathering, mechanical erosion, and thermal stress, which contribute to altering rock properties over time [47]. Beneath it lies the third layer, representing the fresh basement, characterized by significantly higher velocities, ranging from 2,400 to 6,876 m/s. This increase in velocity indicates a more solid and intact geological formation compared to the overlying weathered layer. The significant range of seismic velocities detected in the fractured and massive basement layers underscores the pronounced heterogeneity of bedrock strength within the study area. This variability results from differences in mineral composition, structural features, and the extent of weathering [48,49]. Variations in mineralogical content, such as feldspar vs quartz, can significantly affect the mechanical properties of the rock [50], while structural features like fractures or fault zones can further influence the strength [51].

Figure 16 (a) Variations in P-wave velocity in the study area, (b) variations in soil thickness in the study area, (c) distribution of S-wave velocity in the study area, (d) distribution of density in the study area, (e) Poisson’s ratio variation in the study area, (f) shear modulus variation in the study area, (g) bulk modulus variation in the study area, (h) Young modulus variation in the study area, (i) material index variation in the study area, (j) concentration index variation in contour lines in the study area, (k) ultimate bearing capacity variation in contour lines in the study area, and (l) stress ratio variation in contour lines in the study area.
Figure 16 (a) Variations in P-wave velocity in the study area, (b) variations in soil thickness in the study area, (c) distribution of S-wave velocity in the study area, (d) distribution of density in the study area, (e) Poisson’s ratio variation in the study area, (f) shear modulus variation in the study area, (g) bulk modulus variation in the study area, (h) Young modulus variation in the study area, (i) material index variation in the study area, (j) concentration index variation in contour lines in the study area, (k) ultimate bearing capacity variation in contour lines in the study area, and (l) stress ratio variation in contour lines in the study area.
Figure 16 (a) Variations in P-wave velocity in the study area, (b) variations in soil thickness in the study area, (c) distribution of S-wave velocity in the study area, (d) distribution of density in the study area, (e) Poisson’s ratio variation in the study area, (f) shear modulus variation in the study area, (g) bulk modulus variation in the study area, (h) Young modulus variation in the study area, (i) material index variation in the study area, (j) concentration index variation in contour lines in the study area, (k) ultimate bearing capacity variation in contour lines in the study area, and (l) stress ratio variation in contour lines in the study area.
Figure 16 (a) Variations in P-wave velocity in the study area, (b) variations in soil thickness in the study area, (c) distribution of S-wave velocity in the study area, (d) distribution of density in the study area, (e) Poisson’s ratio variation in the study area, (f) shear modulus variation in the study area, (g) bulk modulus variation in the study area, (h) Young modulus variation in the study area, (i) material index variation in the study area, (j) concentration index variation in contour lines in the study area, (k) ultimate bearing capacity variation in contour lines in the study area, and (l) stress ratio variation in contour lines in the study area.
Figure 16

(a) Variations in P-wave velocity in the study area, (b) variations in soil thickness in the study area, (c) distribution of S-wave velocity in the study area, (d) distribution of density in the study area, (e) Poisson’s ratio variation in the study area, (f) shear modulus variation in the study area, (g) bulk modulus variation in the study area, (h) Young modulus variation in the study area, (i) material index variation in the study area, (j) concentration index variation in contour lines in the study area, (k) ultimate bearing capacity variation in contour lines in the study area, and (l) stress ratio variation in contour lines in the study area.

Moreover, the S-wave velocities were derived from the corresponding P-wave data, resulting in values ranging from 176 to 553 m/s, as illustrated in Figure 16c. In this context, the black line on the map indicates the wadi boundaries. The velocity values increase in the southwest within the study area, and a noticeable increase in S-wave velocity is observed. This increase suggests a strengthening of the subsurface materials in that direction.

The density parameter is considered one of the vital geotechnical indicators used to evaluate and differentiate the physical state of the soil [52,53]. Soil density can provide insights into compaction, porosity, and moisture content, which are crucial for assessing soil suitability for construction and agriculture [54,55]. In this study, soil density in the examined area ranged from 1.29 to 1.7 g/cm3. The analysis indicated that soil density increases progressively toward the wadi in the study area’s southwest while decreasing toward the northeast. The soil is relatively thin and brittle in the northeastern part, exhibiting lower density values than other regions, as illustrated in Figure 16d. This variation in density provides insight into the soil’s structural composition and behavior across the area [56]. The distribution of Poisson’s ratio for the soil, as shown in Figure 16e, ranges from 0.235 to 0.238. The values exhibit a distinct trend of gradually increasing toward the northeastern part of the study area. Conversely, a relative decrease in Poisson’s ratio is noted in the central region of the valley. This variation indicates changes in the soil’s mechanical properties, reflecting the material’s response to stress and strain under different loading conditions [57,58]. The shear modulus, also known as the modulus of rigidity, is an essential geotechnical parameter that measures a material’s resistance to deformation under shear stress, playing a critical role in evaluating soil behavior during seismic events and construction projects [59,60]. The study area’s shear modulus values range from 4 × 108 to 5.2 × 109 dyn/cm2. As illustrated in Figure 16f, the contour map reveals increased shear modulus values toward the wadi southwest of the study area. This rise in shear modulus values suggests that the soil in this region is more rigid and stable, as higher shear modulus values typically correlate with greater resistance to deformation [61,62]. The higher shear modulus indicates that the soil is more resistant to lateral deformation, making it suitable for construction, especially in areas requiring stable foundations [63]. The higher shear modulus in the valley area implies that the soil can bear greater loads and provides improved stability for foundations, minimizing the risks of excessive settlement or lateral displacement under shear forces [64,65].

The bulk modulus is a geotechnical parameter that measures a material’s resistance to uniform compression, providing important information about the compressibility and stiffness of the material [66,67]. It offers valuable insight into how soil or rock will behave under pressure, particularly in assessing soil response during loading conditions in engineering and geotechnical applications [68,69]. In the study area, bulk modulus values range from 6.3 × 108 to 8.2 × 109 dyn/cm2. As depicted in Figure 16g, the contour map shows a notable increase in bulk modulus values toward the valley in the southwest region. The increase in bulk modulus indicates that the soil in this area is more rigid and stable, as higher bulk modulus values generally correlate with greater resistance to compression [70,71]. The higher bulk modulus suggests the soil can better resist compression and deformation. It is ideal for construction projects requiring solid foundational support, particularly in areas subject to high load-bearing demands [72,73].

Young’s modulus, also known as the modulus of elasticity, is a crucial geotechnical parameter that defines the stiffness of a material, indicating how it responds to axial loads or compression [74,75]. It represents the material’s ability to resist deformation under stress, making it fundamental for evaluating soil and rock behavior in geotechnical engineering, particularly in designing foundations and structures [76,77]. Young’s modulus values range from 9.9 × 108 to 1.3 × 1010 dyn/cm2 in the study area. As illustrated in Figure 16h, the contour map reveals a significant increase in Young’s modulus values toward the valley in the southwest region. This rise in Young’s modulus suggests that the soil in this area becomes progressively stiffer and more resistant to deformation, which is critical for assessing its suitability for construction and load-bearing applications [78,79]. The higher Young’s modulus in the valley implies that the soil can support heavier structures and is less likely to experience significant settlement or lateral movement under loading conditions. This makes it ideal for foundational support in construction [80,81].

The material index is a significant geotechnical parameter used to evaluate and classify soils and rocks based on their physical and mechanical properties [82,83]. It plays a critical role in understanding how these materials behave under different environmental and loading conditions, providing insights into their stability, strength, and suitability for construction [81,84]. In the study area, the material index values range from 0.049 to 0.062. As illustrated in Figure 16i, the contour map shows a marked increase in material index values toward the valley in the southwest region. This increase in material index indicates that the soil in this zone possesses more favorable characteristics for construction and load-bearing applications [85,86]. The higher material index values suggest a stronger and more stable material with enhanced resistance to deformation and better support for structural loads [87,88]. This increase in the wadi likely reflects changes in the subsurface composition, which makes the area more suitable for engineering applications such as foundation design and slope stability analysis [89,90].

The concentration index is an essential geotechnical parameter to measure the distribution and concentration of specific materials within soils and rocks, such as minerals and particles [91,92]. It provides crucial insights into the spatial variations in subsurface materials’ physical and chemical properties, influencing their geotechnical behavior, including compaction, permeability, and strength [93,94]. In the study area, concentration index values range from 5.209 to 5.263. As shown in the contour map (Figure 16j), a notable increase in these values occurs toward the valley in the southwest region. This increase indicates a higher concentration of specific materials in that zone, suggesting enhanced soil composition [95,96]. These elevated concentration index values imply improved soil stability, strength, load-bearing capacity, and drainage characteristics, making the southwest area more favorable for construction and development [97,98].

The ultimate bearing capacity is a critical geotechnical parameter indicating that the maximum load per unit area soil can safely support before shear failure occurs [99,100]. This measurement is essential for foundation design and various structural applications, as it helps engineers assess the soil’s ability to support loads without causing excessive settlement or failure [99,101]. In the study area, ultimate bearing capacity values range from 0.22 to 6.2 kg/cm2, with a significant increase observed toward the valley in the southwest region, as shown in Figure 16k. This increase suggests that the soil can support heavier loads, reflecting improved stability and strength due to changes in soil composition, such as increased density or cohesion [100,102]. Such improvements in soil properties are essential for ensuring structural stability and optimizing foundation design [103].

The stress ratio is an important parameter that defines the relationship between various stress components within the soil [98]. Understanding these ratios is vital for analyzing soil behavior, designing foundations, retaining structures, and evaluating slope stability, as they influence the soil’s response to applied loads and its potential for shear failure [99]. In the study area, stress ratio values range from 0.3064 to 0.3116, with a notable decrease observed toward the wadi in the southwest region, as illustrated in Figure 16l.

Based on the recommendations of the National Earthquake Hazards Reduction Program [37], the study area is classified into three soil types according to S-wave velocity, which significantly affects seismic site response and construction suitability, as shown in Table 3.

Table 3

Seismic site characterization depending on shear wave velocity (Safety, 2004)

Site class S-velocity (Vs) (ft/s) S-velocity (Vs) (m/s)
A (Hard rock) >5,000 >1,500
B (Rock) 2,500–5,000 760–1,500
C (Very dense soil and soft rock) 1,200–2,500 360–760
D (Stiff soil) 600–1,200 180–360
E (Soft clay soil) <600 <180
F (Soil requiring additional response) <600, and meeting some additional conditions <180, and meeting some additional conditions

Class E: Soils with S-wave velocities below 180 m/s are considered loose or soft soils. These soils are generally highly compressible and susceptible to amplifying seismic waves. In the study area, Class E soils are mainly found in the northeastern region of the Wadi. Due to their low strength and high susceptibility to deformation under seismic activity, these soils are generally deemed unsuitable for construction, as they pose significant risks to structural stability.

Class D: Soils with S-wave velocities between 180 and 360 m/s are characterized as stiff soils in Class D. This soil type exhibits more excellent deformation resistance than Class E and is typically found in the eastern to southwestern parts of the study area. Stiff soils offer a more stable foundation for structures, making them more suitable for construction, particularly in areas prone to seismic activity.

Class C: Soils with S-wave velocities ranging from 360 to 553 m/s are classified as very dense or hard soils under Class C. This soil, located mainly in the central wadi region, exhibits high rigidity and low compressibility, providing excellent building support. Due to their stability and resistance to seismic wave amplification, Class C soils are highly favorable for construction.

These findings directly impact engineering decisions, urban planning, and sustainability where Class E soil in the northeastern region exhibits low bearing capacity and high compressibility, making it unsuitable for direct construction. Potential engineering solutions include ground improvement techniques such as soil compaction, geotextile reinforcement, or deep foundation methods (e.g., piles or stone columns) to enhance stability. These findings are essential for urban planners when selecting stable areas for large-scale construction. Regions classified as Class C and D are suitable for high-rise buildings and critical infrastructure (e.g., bridges, highways). In contrast, Class E areas should be reserved for low-load infrastructure, parks, or green spaces to prevent future structural failures. By integrating seismic refraction and tomography, this study offers a non-invasive, cost-effective alternative to traditional soil sampling, thereby minimizing environmental disruption. The findings also aid in optimizing land use by identifying areas with low geotechnical risks, ultimately promoting sustainable urban development in Southern Saudi Arabia.

While MSAW, seismic refraction, and SRT techniques offer valuable insights into subsurface conditions, they have certain limitations. Lateral variations can influence their results in subsurface properties and have limited resolution for detecting small-scale subsurface features. Additionally, although highly accurate, borehole (BH) data are expensive and provides localized validation rather than continuous subsurface coverage, which represents a significant limitation in this study.

At the end, drilling geotechnical BHs, doing lab work, and using 3D modeling are recommended for the future extended work in the study, as they will provide subsurface lithology details.

Acknowledgments

The authors would like to thank the editor and associate editor of Open Geosciences Journal for their help and valuable cooperation. They also thank the Researchers Supporting Project number (RSP2025R432), King Saud University, Riyadh, Saudi Arabia, for funding this article.

  1. Funding information: This research article was funded by Researchers Supporting Project number (RSP2025R432), King Saud University, Riyadh, Saudi Arabia.

  2. Author contributions: S.Q. and K.A. contributed to the conceptualization, preparation of the manuscript, and revision process. S.Q., A.A., and K.A. contributed to preparing the manuscript and discussion.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: The datasets are available from the corresponding author on reasonable request.

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Received: 2024-10-10
Revised: 2025-03-16
Accepted: 2025-04-04
Published Online: 2025-05-28

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
  27. Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
  29. Estimating Bowen ratio in local environment based on satellite imagery
  30. 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
  32. Study on the mechanism of plant root influence on soil properties in expansive soil areas
  33. Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
  37. Cross-dimensional adaptivity research on a 3D earth observation data cube model
  38. Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
  39. Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
  40. Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  124. Stability analysis of valley-type upstream tailings dams using a 3D model
  125. Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
  126. Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
  127. Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
  128. A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
  129. Study on the formation mechanism of the hard-shell layer of liquefied silty soil
  130. Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
  131. Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
  132. Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
  133. High-frequency cycles drive the cyclical enrichment of oil in porous carbonate reservoirs: A case study of the Khasib Formation in E Oilfield, Mesopotamian Basin, Iraq
  134. Reconstruction of digital core models of granular rocks using mathematical morphology
  135. Spatial–temporal differentiation law of habitat quality and its driving mechanism in the typical plateau areas of the Loess Plateau in the recent 30 years
  136. A machine-learning-based approach to predict potential oil sites: Conceptual framework and experimental evaluation
  137. Effects of landscape pattern change on waterbird diversity in Xianghai Nature Reserve
  138. Research on intelligent classification method of highway tunnel surrounding rock classification based on parameters while drilling
  139. River morphology and tectono-sedimentary analysis of a shallow river delta: A case study of Putaohua oil layer in Saertu oilfield (L. Cretaceous), China
  140. Review Articles
  141. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  142. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  143. Ore-controlling structures of granite-related uranium deposits in South China: A review
  144. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  145. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  146. Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
  147. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  148. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  149. Special Issue: Geospatial and Environmental Dynamics - Part II
  150. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  151. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  152. Minerals for the green agenda, implications, stalemates, and alternatives
  153. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  154. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  155. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  156. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  157. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  158. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  159. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  160. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  161. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  162. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  163. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  164. Complex multivariate water quality impact assessment on Krivaja River
  165. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  166. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  167. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  168. Flash flood potential index at national scale: Susceptibility assessment within catchments
  169. SWAT modelling and MCDM for spatial valuation in small hydropower planning
  170. Disaster risk perception and local resilience near the “Duboko” landfill: Challenges of governance, management, trust, and environmental communication in Serbia
https://doi.org/10.1515/geo-2025-0799
Keywords for this article
geotechnical properties; sustainable development; Saudi Arabia
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
  27. Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
  29. Estimating Bowen ratio in local environment based on satellite imagery
  30. 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
  32. Study on the mechanism of plant root influence on soil properties in expansive soil areas
  33. Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
  37. Cross-dimensional adaptivity research on a 3D earth observation data cube model
  38. Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
  39. Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
  40. Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  124. Stability analysis of valley-type upstream tailings dams using a 3D model
  125. Variation characteristics and attribution analysis of actual evaporation at monthly time scale from 1982 to 2019 in Jialing River Basin, China
  126. Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang
  127. Investigating spatiotemporal patterns for comprehensive accessibility of service facilities by location-based service data in Nanjing (2016–2022)
  128. A pre-treatment method for particle size analysis of fine-grained sedimentary rocks, Bohai Bay Basin, China
  129. Study on the formation mechanism of the hard-shell layer of liquefied silty soil
  130. Comprehensive analysis of agricultural CEE: Efficiency assessment, mechanism identification, and policy response – A case study of Anhui Province
  131. Simulation study on the damage and failure mechanism of the surrounding rock in sanded dolomite tunnels
  132. Towards carbon neutrality: Spatiotemporal evolution and key influences on agricultural ecological efficiency in Northwest China
  133. High-frequency cycles drive the cyclical enrichment of oil in porous carbonate reservoirs: A case study of the Khasib Formation in E Oilfield, Mesopotamian Basin, Iraq
  134. Reconstruction of digital core models of granular rocks using mathematical morphology
  135. Spatial–temporal differentiation law of habitat quality and its driving mechanism in the typical plateau areas of the Loess Plateau in the recent 30 years
  136. A machine-learning-based approach to predict potential oil sites: Conceptual framework and experimental evaluation
  137. Effects of landscape pattern change on waterbird diversity in Xianghai Nature Reserve
  138. Research on intelligent classification method of highway tunnel surrounding rock classification based on parameters while drilling
  139. River morphology and tectono-sedimentary analysis of a shallow river delta: A case study of Putaohua oil layer in Saertu oilfield (L. Cretaceous), China
  140. Review Articles
  141. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  142. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  143. Ore-controlling structures of granite-related uranium deposits in South China: A review
  144. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  145. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  146. Advancements in machine learning applications for mineral prospecting and geophysical inversion: A review
  147. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  148. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  149. Special Issue: Geospatial and Environmental Dynamics - Part II
  150. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  151. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  152. Minerals for the green agenda, implications, stalemates, and alternatives
  153. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  154. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  155. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  156. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  157. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  158. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  159. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  160. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  161. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  162. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  163. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  164. Complex multivariate water quality impact assessment on Krivaja River
  165. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  166. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  167. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  168. Flash flood potential index at national scale: Susceptibility assessment within catchments
  169. SWAT modelling and MCDM for spatial valuation in small hydropower planning
  170. Disaster risk perception and local resilience near the “Duboko” landfill: Challenges of governance, management, trust, and environmental communication in Serbia
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