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Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application

  • Shengming Chen , Meiling Ji EMAIL logo , Zeyou Chen and Yong Xiang
Published/Copyright: May 22, 2024
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Abstract

The conflict between the supply and consumption of water resources (WR) is a growing concern, and water scarcity has become a major obstacle to the sustainable development of Chinese cities. To address this issue, the resilience assessment regulation model has proven to be an effective tool for decision makers. This model helps them determine how to improve the resilience of WR building systems and mitigate potential risks and threats. The aim of this article is to explore the evaluation and regulation of the resilience of WR building systems, with the goal of alleviating the WR crisis and promoting sustainable development. Through the construction of an evaluation index system, determination of index weights, design of evaluation models, and application of case studies, the fuzzy comprehensive evaluation method was employed to assess the resilience of WR systems. Additionally, an evaluation regulation model was established, along with the proposed regulation method. The evaluation index system consists of four dimensions: flexibility, adaptability, resilience, and fault tolerance.

1 Introduction

Water resource (WR) building system refers to a series of water conservancy facilities and WR management systems, including reservoirs, sluice gates, hydropower stations, water transmission pipelines, water conservancy projects, etc. These facilities and systems are essential for safeguarding human production and life, maintaining social stability, and promoting economic development. However, WR building systems also face various uncertain factors and risks, such as climate change, WR shortages, natural disasters, etc. These factors and risks may seriously affect the stable operation and development of the system. For improving the resilience of WR building systems and addressing various uncertain factors and risks, it is necessary to evaluate the resilience of the system and formulate the corresponding regulatory strategies. The traditional evaluation methods for WR building systems mostly use quantitative analysis methods, such as mathematical models and statistical analysis. However, these methods are often difficult to handle the ambiguity and uncertainty between evaluation indicators and cannot comprehensively assess the resilience of the system. The regulation model for evaluating the resilience of WR building systems based on fuzzy comprehensive evaluation (FCE) is an evaluation method based on fuzzy mathematics theory, which can comprehensively consider the impact of multiple factors on the resilience of WR building systems. The flexibility evaluation and control model for WR building systems based on FCE method can comprehensively consider multiple evaluation indicators and handle the fuzziness and uncertainty among evaluation indicators, thereby obtaining comprehensive evaluation results. The model can be applied to different types of WR building systems, with high applicability and flexibility. Using this model to evaluate the resilience of WR building systems can effectively guide system regulation and improve the resilience and stability of the system, thereby ensuring the normal operation and development of WR building systems. The model can be applied to the toughness evaluation and regulation of WR building systems. For the planning, design, construction, and management of WR building systems, the model can be used to assess the resilience of the system, formulate corresponding response measures, and improve the system’s disaster resistance and adaptability.

Water resource systems (WRS) are important basic resources for human survival and development, and WR building systems are important facilities to ensure the sustainable use and safe supply of WR. However, when faced with adverse factors such as natural disasters and man-made destruction, WR building systems often have insufficient resilience, which can easily lead to serious consequences such as facility damage and water pollution. In the toughness evaluation of WR building system, the FCE method can comprehensively evaluate multiple factors that affect the toughness of WR building systems and obtain the system’s toughness evaluation results. In order to fully maximize the expected WR ecosystem and socio-economic benefits of the siltation dam array, Gao et al. developed a siltation dam benefit maximization model for the first time. The maximum benefit model for silting dams was first applied to the Sijiagou basin [1]. By situating legitimacy and trust within a postcolonial theoretical framework, Jackson challenged the fundamentals of Australia’s water governance system and the assumptions of neutrality that underpin the principles of free participation and inclusive water management [2]. Di Baldassarre et al. aimed to reduce global inequality and achieve a sustainable future for all humanity. Achieving the goal of sustainable water development requires an integrated approach to the management and allocation of WR, involving all actors and stakeholders, and considering how WRS connects different sectors of the society [3]. The resilience evaluation and control model of WR building systems based on FCE method can provide reference for the resilience evaluation and improvement of WR building systems.

The flexibility evaluation and control model for WR building systems can expand the application of FCE method in the field of WR building systems and encourage the promotion and application of this method in other fields. Yang et al. used hierarchical clustering analysis to filter the evaluation indicators and established a comprehensive assessment index system for WR capacity according to the barrier degree model, to identify the key elements that affect the carrying capacity of WR in Weifang [4].

Global warming is considered to be the main cause of adverse effects on climate change and other anomalies, such as unavailability of WR, reduced agricultural production, food security, rising sea water levels, melting glaciers, and loss of biodiversity. Through increasing storage capacity (or rainwater storage), equitable water supply and distribution policies, river health, and water management for watershed management, the negative impacts of climate change on water availability can be reduced [5]. However, they did not conduct a detailed analysis of the evaluation index weights of the WR building system resilience evaluation regulation model.

In order to evaluate the resilience of WR building systems and improve their resilience level, based on the FCE method and combined with the characteristics of WR building systems, this study constructed a resilience evaluation and control model for WR building systems. The model comprehensively considers multiple factors such as flexibility, adaptability, resilience, and fault tolerance of the WR system. Through the quantitative and FCE of each factor, a toughness score for the WR building system is obtained, and some improvement suggestions are proposed. In practical applications, this model can provide a reference for the toughness evaluation and improvement of WR building systems. The innovation of this article is that it not only studied its primary factors, but also analyzed the weight of its secondary factors.

2 WR building system based on FCE method

2.1 FCE method

The structural flow of this article is shown in Figure 1.

Figure 1 
                  Structural flow chart.
Figure 1

Structural flow chart.

2.1.1 Establishment of fuzzy judgment matrix

Fuzzy judgment matrix is an important tool in FCE, which is used to quantify the relationship between multiple evaluation indicators. In the evaluation process, the most important step is to construct a fuzzy judgment matrix. In order to achieve the quantification of judgment analysis, the traditional 1–9 scale method is used to construct a pairwise comparison judgment matrix, that is, using integers between 1 and 9 to express the results of the comparison between the two factors, and using triangular fuzzy numbers to express the judgment matrix obtained by various experts through the scoring method.

Triangular fuzzy numbers are often used in the construction of fuzzy judgment matrices in FCE methods to represent the relative importance of various evaluation indicators. When constructing a fuzzy judgment matrix, experts or decision-makers can convert the relative importance of various indicators into triangular fuzzy numbers based on their subjective judgments, thereby constructing a fuzzy judgment matrix. The triangular ambiguity function θ ji can be defined as

(1) θ ji = ( Z ji , N ji , V ji ) ,

(2) Z ji = min ( x jir ) ,

(3) N ji = r = 1 x x jir x ,

(4) V ji = max ( x jir ) ,

where Z ji , N ji , and V ji represent the lower bound, median, and upper bound of θ ji , respectively; and x jir represents the judgment of the rth expert on the relative importance of factors x j and x i .

The fuzzy judgment matrix obtained from the above formulas is

(5) X = [ x ji ] = X 1 X 2 X m X 1 X 2 X m 1 x 12 x 1 m x 21 x m 1 1 x m x 2 m 1 ,

where x ji represents the relative importance of factors A j and A i .

2.1.2 Hierarchical single sort

The hierarchical single ranking method uses a single evaluation matrix to determine the relative importance of each factor. To solve this problem, the maximum eigenvalue and eigenvector of the decision matrix are solved using MATLAB software. The hierarchical single ranking method decomposes the decision-making problem into multiple levels and multiple criteria, making the decision-making problem more structured and manageable. This helps decision-makers better understand the nature of the problem and the relationship between various factors, thereby making decisions more accurately.

2.1.3 Testing the consistency of judgment matrix

The purpose of consistency testing is to check whether there are contradictions or inconsistencies in the comparison of various factors by decision makers. If there is any inconsistency, it would affect the accuracy of the weight calculation and decision-making results.

Set the consistency index as EJ and the random consistency ratio as EK, and perform consistency checks on the evaluation matrix to ensure the consistency of the evaluation opinions.

Perform a consistency check on the final weight ranking results, and the calculation formula is as follows:

(6) EJ = j = 1 x x j E J j ,

where E J j represents the consistency value of each matrix in the middle layer.

The expression formula for the overall consistency indicator is

(7) KJ = j = 1 x x j K J j .

Among them there is the random consistency test for each matrix in the middle layer.

To test the consistency, it is necessary to calculate the consistency index EJ and the random consistency ratio EK as follows:

(8) EJ = ( β max m ) / ( m 1 ) ,

(9) EK = EJ / KJ ,  

where m is the order of the decision matrix, and KJ is the average random compatibility index usually obtained from table lookup. Generally, when EK < 0.1, the conclusions reached are acceptable; when EK > 0.1, the judgment matrix must be reconstructed until the consistency requirements are met.

2.1.4 Total hierarchical sorting

The hierarchical total ranking refers to the calculation of the weights of the lowest level indicators relative to the target layer, which requires a total ranking based on the hierarchical unit ranking. The hierarchical total ranking method is an effective decision-making evaluation method, which can be applied to various decision-making issues in various fields, such as economy, environment, society, and so on. This article can combine the corresponding weights to obtain this result. The specific calculation formula is as follows:

(10) s r = s j s ji s jir ,

where s r is the weight of the target layer corresponding to the indicators in the rth level, and s j is the weight of the criteria layer indicators relative to the target layer; s ji is the weight of the corresponding sub-criterion layer index relative to the target layer, and s jir is the weight of the rth sub-criterion layer index relative to the target layer.

2.2 Flexibility of WR building system based on FCE method

The resilience evaluation and regulation of WR systems is the key to achieving sustainable development of WR [6,7]. Resilience refers to the adaptability and resilience of WR systems in the face of shocks or disasters. Therefore, studying the resilience evaluation model of WR building systems is of great significance for improving the ability of cities to withstand external shocks and ensuring urban safety [8,9]. Based on the theory of toughness, this study constructed a toughness evaluation index system for WR building systems and used AHP and FCE methods to determine the index weight.

In practical applications, evaluating the resilience of WR systems requires the consideration of multiple factors, such as climate change, natural disasters, and human activities [10,11]. With global warming and changes in precipitation patterns, the supply of WR would also change, and the water crisis will become increasingly severe. Therefore, when evaluating the resilience of WR systems, there is a need to strengthen the consideration of climate change and to develop corresponding coping strategies, to alleviate the impact of climate change on WR systems. In addition, in order to improve the resilience of WR systems and protect the ecological environment, various measures need to be taken to maintain the sustainable use of WR [12,13]. These measures include strengthening WR management and scheduling, rationally utilizing WR, improving WR utilization efficiency, improving the ecological environment, and protecting water sources and water ecosystems. At the same time, when constructing WR projects, consideration should also be given to coordination and balance with the natural environment to avoid irreversible ecological damage. In addition, the adoption of new technologies, materials, and processes can also improve the resilience of WR systems. Moreover, the use of water-saving equipment and water-saving irrigation techniques can reduce the waste of WR and increase the effectiveness of their use, thus enhancing the resilience of WR systems.

To sum up, evaluating and improving the resilience of WR systems is an important means to ensure the sustainable use of WR and promote sustainable development. It is necessary to strengthen multidisciplinary research cooperation and carry out theoretical exploration and practical innovation, to provide scientific basis and support for solving the WR crisis [14,15].

3 Resilience evaluation and regulation model of WR building system and its application

WR building system refers to the combination of building systems with WR management, utilization, and protection through modern scientific and technological means to maximize the conservation and utilization of WR. WR building systems can promote economic development and improve comprehensive benefits through the construction of reservoirs, hydropower stations, water conservancy projects, and other facilities. It can provide important support for socio-economic development and ecological environment protection and is an important construction project. The system mainly includes four aspects: WR collection, water supply, drainage, and reuse. In the experiment, this work established a WR building system model. The FCE method was used to design various weight factors that affect its toughness, and through data analysis and comparison, the optimal WR utilization scheme was found to improve the WR utilization efficiency of the building system. The evaluation indicators of the model include the flexibility, adaptability, resilience, and fault tolerance of the system. Through comprehensive consideration of these indicators, the evaluation results of the resilience of the WR building system can be obtained [16,17].

This model can be applied to the evaluation and regulation of the resilience of WR building systems. In practical applications, through the analysis of the evaluation results, control strategies can be determined to improve the resilience of the system to cope with various uncertain factors and risks. For example, in the face of uncertain factors such as climate change and natural disasters, the system’s resource allocation and management strategies can be adjusted to improve its resilience, thereby ensuring the stable operation of the WR building system.

The model has the following advantages: It can comprehensively consider multiple evaluation indicators and obtain comprehensive evaluation results. Based on the FCE method, it can handle the fuzziness and uncertainty between evaluation indicators. It can be applied to different types of WR building systems, with high applicability and flexibility [18,19].

The model has the following shortcomings: The data demand of the model is high, and a large amount of data related to WR building systems need to be collected. The establishment and use of models require professional knowledge and skills, which is difficult for professionals to use. The interpretability of the model results is relatively low, and in practical applications, it is necessary to interpret and analyze them in combination with actual situations.

3.1 Experimental design

3.1.1 Variable design

The primary evaluation indicators of the resilience evaluation and control model for WR building systems include: elasticity (A), adaptability (B), resilience (C), and fault tolerance (D) of the WR building system. The secondary evaluation indicators of system flexibility include WR allocation flexibility (A1), facility and equipment operation flexibility (A2), economic operation flexibility (A3), and management decision-making flexibility (A4). The secondary evaluation indicators for system adaptability include WR adaptability (B1), facility and equipment adaptability (B2), economic operation adaptability (B3), and management decision-making adaptability (B4). The secondary evaluation indicators for system resilience include WR resilience (C1), facility and equipment resilience (C2), economic operation resilience (C3), and management decision-making resilience (C4). The secondary evaluation indicators for system fault tolerance include WR fault tolerance (D1), facility equipment fault tolerance (D2), economic operation fault tolerance (D3), and management decision fault tolerance (D4), as shown in Table 1.

Table 1

Variable design

Primary indicators Variable design Secondary indicators Variable design
Elasticity of WR building systems A Elasticity of WR allocation A1
Flexibility of facility and equipment operation A2
Economic operation elasticity A3
Management decision resilience A4
Adaptability of WR building systems B WR adaptability B1
Adaptability of facilities and equipment B2
Economic operation adaptability B3
Management decision adaptability B4
Resilience of WR building systems C WR resilience C1
Resilience of facilities and equipment C2
Economic operation resilience C3
Management decision resilience C4
Fault tolerance of WR building systems D Fault tolerance of WR D1
Fault tolerance of facilities and equipment D2
Economic operation fault tolerance D3
Management decision tolerance D4

3.1.2 Reliability test

Reliability refers to the consistency of the results obtained after repeated measurements on the same object using the same method. Reliability testing refers to the process of evaluating the reliability of a measurement tool by verifying the consistency and stability of its measurement results. Its function is the stability of the test itself, not the correctness of the test results. Based on the internal consistency reliability analysis results, the items were optimized. The optimization criteria were that the alpha coefficient should be greater than or equal to 0.7. Only in this way can the research be of remarkable significance and meet statistical requirements. At the same time, if the correlation coefficient between a certain item and the total is less than 0.5, and the consistency coefficient increases significantly after canceling the item, then the item should generally be canceled. This article measures the overall reliability of the evaluation system and the reliability of each level 1 indicator. The test results are shown in Table 2. The overall and partial reliability of this scale was greater than 0.7, so it had a good level of reliability.

Table 2

Reliability test results

Inspection items Number of projects Cronbach’s Alpha
Elasticity of the system (A) 4 0.867
Adaptability of the system (B) 4 0.784
Resilience of the system (C) 4 0.824
Fault tolerance of the system (D) 4 0.872

3.1.3 Validity test

Structural validity refers to whether the test results can prove or explain a theory or structure, that is, whether the test data can prove the rationality of the theory or structure. This study used factor analysis to test the structural validity of the questionnaire. Before conducting factor analysis, the questionnaire should first undergo a Kaiser–Meyer–Olkin (KMO) test and a spherical test to determine whether the questionnaire meets the needs of factor analysis. In general research, the value of KMO should be 0.7. Moreover, if the KMO value is lower than 0.5, it indicates that the scale is not suitable for factor analysis. In this prediction test, the KMO value was 0.779, and the approximate Chi square value of Bartlett’s spherical test was 431.839, indicating that these factors can be subjected to factor analysis.

Based on this, this article used the principal component analysis method. The feature value 1 was taken as the intercepted data, and the orthogonal transformation with the largest variance was used. The results obtained are shown in Figure 2.

Figure 2 
                     Factor loads and their characteristic values rotation. (a) Shows the characteristic values of each factor after rotation, and (b) shows the load values of each factor after rotation.
Figure 2

Factor loads and their characteristic values rotation. (a) Shows the characteristic values of each factor after rotation, and (b) shows the load values of each factor after rotation.

From Figure 2, the secondary factor load values of the WR building system, resilience evaluation, and regulation model were all distributed above 0.5, which was sufficient to indicate that the factor validity of this survey had reached a high level, so it can be ensured that the results obtained in the subsequent formal measurement were accurate and effective. The characteristic root of the first level factor of the resilience evaluation and control model for WR building systems was also maintained at 1, which can indicate the effectiveness of this evaluation factor.

3.1.4 Weight calculation

The weight values of each expert were calculated and tested for consistency. Finally, the weight of the toughness evaluation index system was obtained, as shown in Figure 3.

Figure 3 
                     Weights of resilience indicators for WR building systems. (a) The distribution of primary and secondary factor weight values for A and B and (b) the distribution of primary and secondary factor weight values for C and D.
Figure 3

Weights of resilience indicators for WR building systems. (a) The distribution of primary and secondary factor weight values for A and B and (b) the distribution of primary and secondary factor weight values for C and D.

According to Figure 3, the elasticity, adaptability, resilience, and fault tolerance of a WR building system were positively correlated with the resilience of the WR building system. That is to say, the better the elasticity, adaptability, resilience, and fault tolerance of the WR building system, the stronger the resilience of the WR building system. It can also be seen from Figure 3 that among the first level indicators, their weights were ranked from large to small as system elasticity, system adaptability, system resilience, and system fault tolerance, and their weight values were 0.231, 0.185, 0.169, and 0.128. From this, it can be seen that the elasticity of the system had the greatest impact on system resilience among various factors, while the elasticity of WR allocation had the largest weight value among system resilience, with a weight value of 0.342. It can be seen that the most important factor in the elasticity of the system is the elasticity of WR allocation.

3.2 Application of resilience evaluation and regulation model for WR building system

This article selected a building system in X city as the research object. Through evaluating and regulating the resilience level of the system, four experts calculated their evaluation factor scores. This article used the arithmetic mean method to calculate the scores of evaluation indicators at all levels. The results are shown in Figure 4.

Figure 4 
                  First-level evaluation index scores. (a) The expected scores of the first level evaluation indicators of the WR building system resilience evaluation regulation model and (b) the actual expected scores of the first level evaluation indicators of the WR building system resilience evaluation regulation model.
Figure 4

First-level evaluation index scores. (a) The expected scores of the first level evaluation indicators of the WR building system resilience evaluation regulation model and (b) the actual expected scores of the first level evaluation indicators of the WR building system resilience evaluation regulation model.

From Figure 4, it can be seen that the average expected pass scores for elasticity, adaptability, resilience, and fault tolerance of WR building systems are 3, 2.5, 2, and 1.8, but their average actual scores are 2.5, 2.2, 1.8, and 1.6, respectively. From this, it can be concluded that the actual score situation of the city has not achieved the expected effect.

The scores of the secondary evaluation indicators for the resilience evaluation and regulation model of the WR building system are shown in Figure 5.

Figure 5 
                  Grade II evaluation index scores. (a) The scores of the secondary evaluation indicators A1–B4 of the WR building system resilience evaluation and regulation model and (b) the scores of the secondary evaluation indicators C1–D4 of the WR building system resilience evaluation and regulation model.
Figure 5

Grade II evaluation index scores. (a) The scores of the secondary evaluation indicators A1–B4 of the WR building system resilience evaluation and regulation model and (b) the scores of the secondary evaluation indicators C1–D4 of the WR building system resilience evaluation and regulation model.

From Figure 5, it can be seen that the secondary evaluation indicators of the resilience evaluation and control model for the WR building system have also achieved the expected qualified results. It can be seen that there were significant problems in the city’s WR building system. After the resilience evaluation, it was found that the system has low adaptability and resilience in the face of emergencies, and its fault tolerance and resilience also need to be improved.

To address these issues, the following regulatory measures can be formulated.

3.2.1 Strengthening WR management, improving water efficiency, and reducing WR waste

Strengthening WR management is an essential task in WR building systems. Strengthening WR management can improve water use efficiency and reduce WR waste, thereby ensuring the supply of WR to a certain extent. For building systems, the following measures can be taken to strengthen WR management: By controlling the water sources in the system, the quality and quantity of water sources can be ensured to meet the requirements; by strengthening the monitoring of water consumption in the system, problems such as excessive water consumption and waste can be identified and resolved in a timely manner; the maintenance of water consuming equipment can ensure the normal operation of the equipment, thereby avoiding problems such as water waste caused by equipment failures; the consumption of WR can be reduced by introducing water-saving technologies and saving water.

3.2.2 Establishment of emergency plans to improve the system’s ability to respond to emergencies

Establishing emergency plans is one of the important measures to improve the resilience of WR building systems. By establishing an emergency plan, it is possible to respond quickly and take effective measures in the event of an emergency, thus reducing the impact of the event on the system. Specifically, the following measures can be taken to establish an emergency plan: developing an emergency plan, clarifying the responsibilities and response measures of each position, and ensuring rapid response when an event occurs. The maintenance of emergency equipment can ensure the normal operation of the equipment, thereby avoiding problems such as untimely response caused by equipment failures.

3.2.3 Addition of water quality monitoring equipment to detect and solve water quality problems in a timely manner

Water quality issues are one of the common issues in WR building systems. Therefore, installing water quality monitoring equipment is one of the important measures to improve system resilience. Specifically, the following measures can be taken to install water quality monitoring equipment: sampling and testing the water sources in the system to clarify the water quality situation. Based on the water quality situation, the type and quantity of water quality monitoring equipment to be installed are determined. It is necessary to maintain water quality monitoring equipment and ensure its normal operation to avoid problems such as delayed water quality monitoring caused by equipment failures. By analyzing and judging the monitoring results, water quality issues can be identified and resolved in a timely manner.

3.2.4 Strengthening the maintenance and management of the system to ensure the normal operation of the system

Strengthening the maintenance and management of the system is one of the important measures to ensure the normal operation of the system. By strengthening the maintenance and management of the system, it is possible to avoid system failures caused by equipment failures, improper human operations, and other reasons, thereby improving system resilience. Specifically, the following measures can be taken: regular maintenance and repair of system equipment to ensure the normal operation of the equipment. The personnel in the system need to be trained and managed to ensure standardized operations and clear processes. By monitoring and backing up the data in the system, it is possible to avoid system failures caused by issues such as data loss. Through regular inspection and evaluation of the system, problems can be identified and resolved in a timely manner, thereby improving system resilience.

By implementing the above measures, the resilience level of the WR building system can be improved and the normal operation of the system can be ensured. At the same time, it can achieve multiple goals such as saving water and protecting the environment.

4 Conclusion

Based on the FCE method, this study constructed a resilience evaluation and control model for WR building systems and has achieved certain results in practical applications. Through the application of this model, it is possible to evaluate the resilience of WR building systems and propose corresponding improvement suggestions. The model comprehensively considers multiple factors such as sustainable utilization of WR, water quality, safety, emergency capacity, and facility integrity, and its evaluation results are more comprehensive and accurate. At the same time, the model has the advantages of simple operation and wide applicability, which can provide a reference for the toughness evaluation and improvement of WR building systems. However, the improvement and perfection of this model still need further research and exploration. The sample data of the applied resilience evaluation and control model for WR building systems is insufficient to fully reflect the resilience of WR building systems. Model parameter settings are not accurate enough: When building a model, the parameter settings have a significant impact on the accuracy of the model. The parameter settings used in this article may have shortcomings that require further improvement and optimization. The scope of application of the model is limited: The scope of application of the model in this article is mainly limited to the field of WR building systems, and further research and exploration are needed for toughness evaluation in other fields. The application scope of the model can be extended to other fields, such as urban planning, environmental protection, etc., thereby improving the application value and practicality of the model. New technologies such as artificial intelligence and big data can be combined to further improve the accuracy and efficiency of the model.

  1. Funding information: The authors state no funding involved.

  2. Author Contributions: Zeyou Chen and Yong Xiang: Editing data curation, Supervision. Shengming Chen and Meiling Ji: Writing-original draft preparation. Shengming Chen: conceived the experiment. Yong Xiang, Meiling Ji, and Zeyou Chen: conducted the experiment(s). Meiling Ji and Zeyou Chen: analysed the results. All authors reviewed the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

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Received: 2023-06-14
Revised: 2023-10-13
Accepted: 2023-10-26
Published Online: 2024-05-22

© 2024 the author(s), published by De Gruyter

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

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  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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