Home Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
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Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis

  • Fei Teng , Yimin Mao , Yican Li EMAIL logo , Subin Qian and Yaser A. Nanehkaran EMAIL logo
Published/Copyright: May 31, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Naqadeh Region (NR) is one of the most sensitive regions regarding geo-hazards ‎occurrence in Northwest of Iran. The landslides triggering parameters that ‎identified for the studied region are classified as elevation, aspect, slope angle, ‎lithology, drainage density, distance to river, weathering, land-cover, ‎precipitation, vegetation, distance to faults, distance to roads, and distance to ‎the cities. These triggering factors are selected based on conducting field ‎survey, remote-sensing investigation, and historical development background ‎assessment. Regarding the investigations, 12 large-scale, 15 medium-scale, and 30 small-scale historical landslides ‎(57 in total) were recorded in the NR. The historical landslides were used to provide ‎sensitive area with high probability of ground movements. The objectives of this study are multifaceted, aiming to address critical gaps in understanding and predicting landslide susceptibility in the NR. First, the study seeks to evaluate and compare the effectiveness of ‎support-vector machine (SVM), multilayer perceptron (MLP), and decision tree ‎‎(DT) algorithms in predicting landslide susceptibility. So, as methodology, the ‎presented study used comparative models for landslide susceptibility based on ‎SVM, MLP, and DT approaches. The predictive models were compared based on model ‎accuracy as the area under the curve of the receiver operating characteristic ‎curve. According to the estimated results, MLP is the highest rank of overall ‎accuracy to provide susceptibility maps for landslides in NR. From a perspective of ‎the risk ability, the west and south-west sides of the county were identified within ‎the hazard area.

Keywords: landslide susceptibility; support-vector machine; multilayer ‎perceptron; decision tree; geo-hazards

1 Introduction

Landslides is one of the important geo-hazards were responsible various scale ground deformation, financial damages, and live losses [1] which are the ground movement, and debris down a slope or low topographic levels [2]. They can be caused by natural events such as heavy rainfall, earthquakes, volcanic activity, erosion, or human activities such as construction or mining [3]. Landslides can range in size and severity from small, localized events to large-scale disasters that can cause significant damage to property and infrastructure [4]. There are several different types of landslides based on Varnes [5] and Highland and Bobrowsky [6] including rockfalls, debris flows, toppling, and sliding. Regardless of the type of landslides can have significant impacts on the environment, eco-systems, and human lives [7]. They can also have economic and social impacts, particularly in areas where infrastructure or communities are in landslide-prone areas. Comprehending the origins and triggering factors behind landslides occurrence, alongside implementing efficient management tactics to minimize their repercussions, constitutes a significant focal point for geologists and geoengineers [8].

Landslide susceptibility refers to the potential of an area or region to experience landslides which is determined by a combination of various triggering factors which involves identifying areas that are likely to experience landslides in the future. This information is essential for land-use planning, hazard mitigation, and risk reduction [9,10]. Several methods are used to assess landslide susceptibility which is classified into the quantitative, qualitative, and semi-quantitative procedures [11]. These general groups can be divided into the various sub-groups containing various methods such as deterministic, statistic, probabilistic, heuristic, geostatistic, inventory, and knowledge-based approaches. Each approach has its own advantages and disadvantages to provide the landslide susceptibility maps [12,13]. According to Ercanoglu and Gokceoglu [14], landslide susceptibility assessment has been on the rise worldwide, but there is currently no established overall standard procedure for doing so. Thus, each researcher follows their specific methods to achieve more accurate susceptibility analysis for landslides.

With recent developments and advancements in computer-based modeling, the landslide susceptibility assessments become more straightforward and provide highly accurate/reliable results [3]. In the meantime, the artificial intelligence (AI)-based method received high rate of success in the susceptibility analysis of landslides [12]. There are several advantages of AI-based methods for landslide susceptibility assessment which can be stated as accurate predictions, faster processing, cost-effective, adaptability, improved decision-making, reduced risk, multidisciplinary integration, objective analysis, scalability, automation, and accessibility, constantly evolving and improving efficiently [15,16]. While there are many advantages to using AI-based landslide susceptibility assessment, there are also some limitations that need to be considered [10].

AI-based methods offer several advantages for employing support-vector machine (SVM), multilayer perceptron (MLP), and decision tree (DT) algorithms in predicting landslide susceptibility in the Naqadeh Region (NR). First, these AI algorithms can effectively handle complex and nonlinear relationships within the dataset, which is crucial for capturing the intricate interplay of various factors influencing landslide occurrence [11]. SVMs excel in identifying optimal decision boundaries, making them well-suited for distinguishing between different classes of landslide susceptibility. MLPs, with their ability to learn hierarchical representations of data, can capture subtle patterns and dependencies, enhancing the accuracy of landslide susceptibility models [12]. DT algorithms provide interpretable decision rules, enabling stakeholders to understand the factors driving landslide susceptibility in the NR region and facilitating informed decision-making for risk mitigation strategies. Moreover, AI algorithms offer scalability and adaptability, allowing for the integration of diverse datasets and variables relevant to landslide susceptibility assessment in the NR region. These algorithms can efficiently process large volumes of spatial and environmental data, including elevation, slope angle, lithology, precipitation, and land cover, enabling a comprehensive analysis of landslide triggers [13]. By leveraging AI techniques, researchers can develop more robust and reliable landslide susceptibility models tailored to the specific characteristics of the NR region, thereby enhancing the effectiveness of landslide risk management efforts. Furthermore, AI algorithms facilitate continuous learning and improvement of landslide susceptibility models over time [14,15]. Through iterative refinement and optimization, SVM, MLP, and DT algorithms can adapt to evolving environmental conditions and incorporate new data sources, enhancing the accuracy and reliability of landslide susceptibility predictions for the NR region. This dynamic capability ensures that landslide risk assessment remains up-to-date and responsive to changing environmental factors, enabling stakeholders to proactively mitigate landslide hazards and safeguard communities and infrastructure in the NR.

The objectives of this study are multifaceted, aiming to address critical gaps in understanding and predicting landslide susceptibility in the NR. By assessing the performance of these AI techniques, the study aims to identify the most suitable modeling approach for accurately characterizing landslide-prone areas in the NR. Additionally, the study aims to enhance the interpretability of landslide susceptibility models by analyzing the contributing factors and spatial patterns identified by each algorithm, thereby providing valuable insights for stakeholders and decision-makers involved in landslide risk management. So, the presented study used a comparative evaluation based on well-known machine learning classifiers including SVM, MLP, and DT. These classifiers have good capabilities in landslide susceptibility assessments, which are used for various cases worldwide. According to the various contributions that published in different literatures, it can be stated that the main advantages of the SVM, MLP, and DT in landslide susceptibility analysis are categorized as follows [17–20].

SVM is effective in high-dimensional spaces and is good at separating data that are not linearly separable. This classifier has a regularization parameter that helps prevent overfitting, making it less prone to errors caused by noise or outliers in the data. Also, SVM can handle both linear and non-linear classification and regression tasks. MLP is a powerful model for non-linear classification and regression tasks. This classifier can learn complex decision boundaries by using multiple hidden layers of neurons and handle both continuous and categorical data, making it a versatile algorithm. Also, MLP is relatively easy to use and implement, and it can be trained using a variety of optimization techniques. DT is easy to understand and interpret, making it useful for explaining decisions to non-technical stakeholders. This classifier can handle both numerical and categorical data, making it a versatile algorithm and capture non-linear relationships between variables.

Lee et al. [21] employed weighted artificial neural networks (ANNs) to assess landslide susceptibility, offering a valuable approach for spatial data handling and processing. They utilized a probabilistic method to determine the learning rate for factors contributing to landslide occurrence. These findings were then leveraged to construct an ANN-driven landslide susceptibility index. In a similar vein, Ermini et al. [22] utilized MLP and the probabilistic neural network, both belonging to the shallow learning category, to evaluate landslide susceptibility in the Riomaggiore catchment, a subwatershed situated within the Northern Apennines’ Reno River basin in Italy. Kanungo et al. [23], Oh and Pardhan [24], Quan and Lee [25], Park et al. [26], and Nourani et al. [27] conducted comparative analyses aimed at forecasting the likelihood of landslide occurrences in mountainous regions, particularly in tropical areas. Their approach involved integrating benchmark classifiers and ANN techniques. The common thread among these studies was the emphasis on enhancing the main database for training and testing sets, along with diversifying benchmark learning methodologies, such as frequency ratio, logistic regression, adaptive neuro-fuzzy inference system, and analytic hierarchy process. In contrast, Liu and Wu [28], and Xiao et al. [29] explored more sophisticated learning approaches for landslide susceptibility mapping, yielding notable advancements over traditional benchmark classifiers (referred to as conventional machine learning or CML) and shallow learning methods. Furthermore, Ortiz and Martínez-Graña [30] and Ghorbanzadeh et al. [31] leveraged convolutional neural networks (CNNs) for landslide susceptibility assessments, resulting in the creation of accurate susceptibility maps. These researchers highlighted the favorable impact of CNNs on satellite imagery, with outputs seamlessly transferable into the geographic information system (GIS) environment. Mutlu et al. [32] employed recurrent neural networks (RNNs) for susceptibility assessments and forecasting landslide-prone regions. While RNNs offer distinct advantages over CNNs, particularly in accuracy and performance, CNNs exhibit better adaptability for the task at hand.

The novelty of the presented study lies in its utilization of comparative modeling techniques for landslide susceptibility assessment. By employing SVM, MLP, and DT approaches, the study provides a comprehensive evaluation of different machine learning algorithms in predicting landslide susceptibility. This comparative approach allows for a thorough analysis of the strengths and weaknesses of each model, enabling researchers to identify the most suitable method for landslide hazard assessment in the specific study area. Furthermore, by assessing model accuracy using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the study ensures a robust evaluation criterion. The AUC-ROC metric provides a quantitative measure of model performance, considering both sensitivity and specificity, thereby offering a more comprehensive assessment of predictive capabilities. This approach enhances the reliability and validity of the findings, facilitating informed decision-making for landslide risk management and mitigation strategies in the NR and similar geographical contexts.

2 Study area

The NR is a district in Northwestern Iran, located in the West Azerbaijan Province. The county encompasses the city of Naqadeh and several surrounding towns and villages [33]. NR is situated on the bank of the Bayzawa River and the Kani Sivin River, encompassing an old artificial mound which is located is to the south-west of Urmia Lake on the lower course of the Gadar river. It is situated in the Zagros Mountains region, which is a complex mountain range that spans western Iran. The region is characterized by its rugged terrain, with steep slopes and deep valleys, providing potential geo-hazards regarding landslides and rockfalls [34]. The location of the NR is illustrated in Figure 1. In terms of climate, NR has a semi-arid to Mediterranean climate, with hot, dry summers and cool, wet winters. The region receives most of its precipitation during the winter months, with the summer months being relatively dry [33]. As a general overview, the geography and geology of the NR make it unique and complex regarding geo-hazards and land-sliding, with rugged mountains, deep valleys, and scenic rivers [34].

Figure 1 Location of the studied region in Iran.
Figure 1

Location of the studied region in Iran.

Also, by using a desk study check, 12 large-scale, 15 medium-scale, and 30 small-scale historical landslides (57 in total) were considered sensitive target points in the NR to increase the accuracy of landslide susceptibility assessment. The landslides chosen for analysis predominantly encompass significant failures within NR. Additionally, smaller-scale landslides frequently occur in proximity to these larger events. By focusing on the primary large-scale landslides, we aim to identify the most sensitive areas of interest, which are pivotal in understanding potential catastrophic impacts. So, the primary aim in selecting these landslides is rooted in the historical geo disaster management plan of the county, which underscores their notable impact on the studied area. By identifying these factors and their relative importance in an NR, it is possible to assess the susceptibility of the region to landslides and to implement measures to mitigate the geo-hazard risk. These measures can include slope stabilization techniques, drainage systems, and land-use planning and management [11].

3 ‎Geological setting

Geologically, the NR is affected by the Zagros fold-thrust belt, which is a result of the collision between the Arabian Plate and the Eurasian Plate. This collision has led to the deformation and folding of the Earth’s crust, resulting in the formation of the Zagros Mountains. The region is characterized by a variety of rock types, including limestone, sandstone, shale, and volcanic rocks. A geological map of the studied region is provided in Figure 2. The rugged terrain and geological features of the area make it susceptible to landslides, particularly during periods of heavy rainfall or seismic activity [35]. In recent years, several landslides have occurred in NR, causing damage to infrastructures and buildings. One notable landslide occurred in 2017 in the village of Ahmadkhel, which is located near Naqadeh city which is recorded as historical landslides. The landslide was triggered by heavy rainfall, and it resulted in the death of six people and the destruction of several homes [36]. To mitigate landslides hazard effects, conducting a landslide susceptibility assessment for the region and one of the key requirements to evaluate the level of risk, ongoing monitoring, and mitigation efforts to minimize their impacts on communities and the environment. This aim is targeted in this article.

Figure 2 Geological map of the studied region.
Figure 2

Geological map of the studied region.

4 ‎Triggering factors

There are several techniques that are used to select and identify the triggering factors of landslides, which are classified as historical development background check, remote-sensing observations, field survey, and desk study check [4,6]. To proceed with the extraction and selection of triggering factors, the present study utilized a field survey, a background check on historical development, and remote-sensing observation. The landslide-triggering parameters that are identified for the NR are classified as elevation, aspect, slope angle, lithology, drainage density, distance to river, weathering, land-cover, precipitation, vegetation, distance to faults, distance to roads, and distance to the cities [6]. To elaborate further, the assessment process involved a detailed examination of the past occurrences of landslides in the area and their potential causes. Site investigations were conducted to gather information on the geological characteristics of the NR, as well as to identify the existing vegetation cover and its condition. Moreover, satellite imagery was used to map the terrain and to assess the suitability of the region for potential landslides. This analysis involved identifying the slope steepness and gradient, the presence of potential unstable soil or rock formations, and the density and condition of vegetation cover. By combining these evaluation methods, a thorough and comprehensive assessment of each triggering factor was conducted to accurately evaluate the susceptibility of the NR to land-sliding probability.

Figure 3 depicts the rasterized triggering factors within a GIS environment, which will be utilized to generate a susceptibility map for the NR. The susceptibility map will facilitate an accurate assessment of the likelihood of landslides in the NR, thereby enabling appropriate infrastructural and urban development planning. These triggering factorial maps that prepared used as basic maps and information layers for comparative modeling by SVM, MLP, and DT. The gathered data will be used as the main database for all prediction processes. In landslide susceptibility analysis for NR, triggering parameters are key factors that contribute to the initiation or occurrence of landslides. These parameters interact with the inherent susceptibility of the terrain and environmental conditions to increase the likelihood of slope failure. Understanding and analyzing these triggering factors are crucial for predicting and mitigating landslide hazards. Let us explore how each of the mentioned triggering parameters relates to landslide occurrence in NR:

Figure 3 The landslide triggering factors for NR: (a) elevation, (b) aspect, (c) slope angle, (d) ‎lithology, (e) drainage density, (f) distance to river, (g) weathering, (h) land-cover, (i) precipitation, ‎‎(j) NDVI, (k) distance to faults, (l) distance to roads, and (m) distance to the cities.
Figure 3

The landslide triggering factors for NR: (a) elevation, (b) aspect, (c) slope angle, (d) ‎lithology, (e) drainage density, (f) distance to river, (g) weathering, (h) land-cover, (i) precipitation, ‎‎(j) NDVI, (k) distance to faults, (l) distance to roads, and (m) distance to the cities.

4.1 Precipitation

One of the most significant triggering factors is precipitation. Heavy rainfall can saturate the soil, increase pore water pressure, and reduce soil cohesion, making slopes more prone to failure. Intense or prolonged rainfall events can trigger landslides, especially in areas with steep slopes and poorly drained soils.

4.2 Slope angle and aspect

Slope angle and aspect influence the stability of slopes. Steeper slopes are inherently more susceptible to landslides, especially when combined with other factors such as weak lithology or intense weathering. Aspect can affect the exposure of slopes to sunlight and rainfall, influencing rates of weathering and erosion.

4.3 Lithology and weathering

The type and composition of underlying rocks or soil (lithology) play a significant role in landslide susceptibility. Some lithological formations in NR are more prone to weathering and erosion, leading to instability (e.g., alluvial, quaternary deposits). Weathering processes weaken rock and soil materials over time, making them more susceptible to failure, particularly under the influence of external triggers like precipitation.

4.4 Drainage density

Poor drainage exacerbates landslide susceptibility by increasing water infiltration and reducing soil strength. High drainage density areas tend to have better natural drainage systems, lowering the risk of landslides compared to regions with poor drainage, where water accumulates and increases pore water pressure.

4.5 Land-cover and vegetation

Vegetation acts as a stabilizing factor by reducing surface erosion, intercepting rainfall, and binding soil particles together. Deforestation or land cover changes can increase landslide susceptibility by removing this protective cover and exposing slopes to erosion. Additionally, land cover can affect surface runoff patterns, further influencing landslide occurrence.

Other factors such as distance to rivers, faults, roads, and cities also influence landslide susceptibility indirectly. Rivers and faults may act as weak zones or pathways for water infiltration, while roads and urbanization can alter natural drainage patterns and increase surface runoff, thereby affecting slope stability. In landslide susceptibility analysis, these triggering parameters are often integrated into geospatial models using remote sensing and GIS techniques to assess the likelihood of landslide occurrence in a given area. By considering these factors collectively, researchers and planners can identify high-risk areas, implement mitigation measures, and develop strategies for sustainable land use planning to reduce the impact of landslides on human lives and infrastructure.

Remote sensing analysis, coupled with GIS, offers a powerful tool for landslide susceptibility assessment in the NR. By leveraging satellite imagery, researchers can obtain high-resolution spatial data capturing various terrain attributes and environmental factors influencing landslide occurrence. Through image processing techniques, such as spectral analysis, land cover, land use, slope characteristics, and vegetation cover can be extracted and analyzed. GIS allows for the integration of these remote sensing-derived datasets with other spatial data layers, such as topography, geology, and hydrology, to create comprehensive landslide susceptibility models. By overlaying these layers and applying statistical or machine learning algorithms, such as logistic regression or random forest, researchers can identify areas at high risk of landslide occurrence based on the spatial distribution and interaction of contributing factors. Moreover, satellite imagery enables temporal analysis, facilitating the monitoring of land cover changes, vegetation dynamics, and slope instability over time, which is essential for assessing long-term landslide susceptibility trends and guiding effective land management strategies in the NR. For example, the normalized difference vegetation index (NDVI) is used as a vegetation index in mapping which is a remote sensing index used to measure the health and density of vegetation cover in an NR. The NDVI index is based on the principle that healthy vegetation absorbs visible light and reflects near-infrared light. NDVI values range from −1 to +1, with higher values indicating denser and healthier vegetation cover [37]. NDVI is calculated using the following formula [38]:

(1) NDVI = [ NIR − Red ] / [ NIR + Red ] ,

where NIR is the near-infrared band reflectance and Red is the red band reflectance [38].

5 Methods

5.1 Comparative models implementation

The presented study used comparative predictive modeling procedures for susceptibility assessments for landslides in NR. The SVM, MLP, and DT were selected for the susceptibility analysis. Provided results were compared and controlled by using well-known verification models. SVM is a supervised learning algorithm that is particularly effective when dealing with high-dimensional and non-linear datasets, making it a popular choice [39] for landslide susceptibility assessments. The following steps are demonstrating the predictive SVM-based model implementation in this article:

Step 1: Data preprocessing is used to gather the necessary data and preprocess it. This involves cleaning and formatting the data, as well as converting categorical variables into numerical values. Additionally, the data need to be normalized to ensure that all input variables are on the same scale.

Step 2: Feature selection which is used to select the relevant triggering factors that influence landslides.

Step 3: Model training after input variables have been selected (triggering factors); the next step is to train the SVM model using a subset of the data.

Step 4: After training the model, it is essential to test its performance on a separate subset of the data that was not used for training.

Step 5: Prediction and mapping which the trained SVM model is used to predict the probability of landslides in NR based on input triggering factors and recorded historical landslides.

MLP is a type of ANN that is commonly used for supervised learning tasks such as classification and regression. It consists of one or more hidden layers of neurons, each of which is connected to the input layer and the output layer [40–48]. Implementing an MLP for a landslide susceptibility assessment task involves several steps that can be categorized into the following steps for NR:

  1. Steps 1 and 2: Same as steps 1 and 2 in SVM modeling,

  2. Step 3: Train-test split which splits the preprocessed dataset into training and testing datasets.

  3. Step 4: Define the number of hidden layers, the number of neurons in each layer, and the activation function used in each neuron.

  4. Step 5: Compile the MLP model by specifying the loss function, optimizer, and metrics to be used during training.

  5. Step 6: Train the MLP model using the training dataset. This involves feeding the training data into the MLP model and updating the weights of the network through backpropagation.

  6. Steps 7 and 8: Same as steps 4 and 5 in SVM modeling.

DT is a popular machine learning algorithm used for classification and regression tasks [49]. It is a tree-like model where each node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome of the decision process [39,40]. Implementing a DT for a landslide susceptibility assessment task involves several steps which can be categorized into the following steps for NR:

  1. Steps 1 and 2: Same as steps 1 and 2 in SVM and MLP modeling.

  2. Step 3: Same as step 3 in MLP modeling.

  3. Step 4: Define the criterion to be used for splitting the data, such as entropy or Gini index.

  4. Step 5: Fit the DT model using the training dataset. This involves recursively splitting the dataset into smaller subsets based on the chosen criterion and hyperparameters.

  5. Steps 6 and 7: Same as steps 4 and 5 in SVM modeling or steps 7 and 8 in MLP modeling.

Table 1 provides the hyperparameters used in this article for each of the mentioned machine learning classifiers for susceptibility analysis of landslides in NR. Hyperparameters are parameters that are not learned from the data during model training but must be set before the training process begins [51–54]. They are often used to control the complexity of the model, regularize the model, and avoid overfitting.

Table 1

The applied predictive models’ hyperparameters

Classifiers Hyperparameters Elements
SVM Kernels Kernel = “poly”; degree = 2
C value C = 100; Epsilon = 0.1
MLP Hidden layers’ size Activation = “relu”
Learning rate Optimization = rmsprop
Optimization Loss_function = “mse”
Metrics = “mae”, “rmse”
DT Max depth Criterion = “gini”; Max_depth = 5
Random state Ccp_alpha = 0.0
Min_samples_leaf = 1
Random_state = 100

It is important to note that the effectiveness of SVM, MLP, or DT for landslide susceptibility assessment depends on several parameters including the quality and quantity of the input data, the choice of feature selection and classification algorithms, and the inherent variability and uncertainty in landslide processes. Therefore, it is essential to carefully evaluate the model’s performance and limitations before using it for decision-making purposes.

5.2 Data preparations

Twelve recorded historical landslides in NR are selected for prone area identification. The aforementioned factors are classified as triggering factors, as they render an area vulnerable to movement without directly causing a landslide. Table 2 provides the main r resource of data prepared for this study. Before these data can be used in susceptibility modeling, they could be subject to ‎multicollinearity and correlated variables. The multicollinearity is a ‎phenomenon in which one predictor variable in a regression model can be predicted linearly ‎from others. To test for multicollinearity, variance inflation factors (VIF) are commonly used ‎‎[10]. The VIF value of more than 5 indicates potential multicollinearity. In ‎this study, all triggering factors produced VIF values less than 2.25 are presented in Table ‎‎2.‎

Table 2

The landslide triggering factors information utilized in this work

Class Triggering factors Resolution (m) Data source VIF index
Morphologic Elevation ±30 DEM 1.25
Slope aspect ±30 DEM 1.27
Slope angle ±30 DEM 1.29
Geologic Lithology ±30 Geological data 1.96
Drainage density ±30 Landsat TM, IWRM* 1.64
Distance to river ±30 Landsat TM, IWRM* 1.54
Weathering ±30 Geological data 1.02
Land cover‎ ±30 Geological data 1.02
Climatologic Precipitation ±30 Landsat TM, IMO† 1.16
NDVI ±30 Landsat TM, IMO† 1.12
Seismicity Distance to faults ±30 Seismic data 1.81
Human-activity Distance to roads ±30 DEM, Google Map 2.25
Distance to the cities ±30 DEM, Google Map 2.17

*Iran Water Resources Management Company (IWRM). †Iran Meteorological Organization (IMO).

Therefore, these factors are responsible for the prevalence of landslides in NR where relevant data can be obtained from available sources, historical development background, and field studies. On the other hand, triggering factors, such as rainfall and earthquakes, initiate landslides by destabilizing the slope and converting it from a marginally stable to an actively unstable state. Generating an accurate landslide inventory map is crucial for establishing the correlation between the 57 historical landslide distributions and triggering factors in the NR. To achieve this, extensive field surveys and observations were conducted in the study area to produce a comprehensive and dependable landslide inventory map. A digital elevation model (DEM), with a resolution of ±30 m, was used to provide morphological maps in the county level. The DEM data were used to prepare elevation, aspect, and slope angle rasterized information layers in a GIS environment. Using geo-units and ground data [34,35] helped to produce the land cover, lithology, distance to faults, and raster layers as well. In this study, a Landsat TM8 and ETM + satellite image was used to provide weathering, vegetation, distance to roads, and distance to the cities, drainage density, distance to river, relative information accordingly. Figure 3 provides the rasterized maps of triggering factors in GIS.

Rasterizing triggering factors in ArcGIS involves converting vector data representing factors such as elevation, slope angle, land cover, and precipitation into raster format, crucial for cohesive analysis. After gathering relevant vector datasets, each is rasterized individually, assigning cell values based on their attributes. Ensuring consistent spatial resolution and alignment among raster layers is essential for seamless integration into a unified analysis framework. Integration of rasterized layers entails overlaying them using GIS operations, facilitating the development of landslide susceptibility maps. Various modeling techniques, including statistical methods and machine learning algorithms, leverage these integrated layers to assign susceptibility values to different areas based on their risk profiles. Validation of susceptibility maps with historical data or field surveys ensures accuracy, providing insights into spatial distribution and the significance of triggering factors. Overall, rasterizing triggering factors and integrating them into GIS-based landslide susceptibility mapping workflows enable comprehensive hazard assessments. This approach supports informed decision-making for land use planning and risk mitigation strategies, aiding in the identification of landslide-prone areas and understanding the factors driving susceptibility in the study region.

5.3 Models implementations and verifications

After providing the database with undertaking various triggering factors data as well as recorded historical landslides for NR. These data were used as primary datasets for predictive modeling via SVM, MLP, and DT classifiers. Models were trained with a training set and tested with a testing set. These sets are provided by random division of primary datasets of input data. The training set was contain 70% of main database and testing set is contain remained 30%. The ROC curve is commonly used to evaluate the performance of predictive models. It is a graph that plots the true positive rate (TPR) against the false positive rate (FPR), which provides a measure of the model’s overall accuracy and its ability to discriminate between positive and negative cases. The ROC curve is a useful tool for evaluating the performance of binary classification models, where the output variable can take on one of two possible values (e.g., positive or negative). The curve is created by varying the discrimination threshold of the model and plotting the TPR against the FPR for each threshold value. The TPR represents the proportion of true positive cases correctly identified by the model, while the FPR represents the proportion of false positive cases incorrectly identified by the model. In this research, the ROC curve was employed as a validation tool to compare the performance of different models for estimated overall accuracy by AUC rates in preparing landslide susceptibility maps. The AUC is often used as a summary statistic of the model’s performance. A perfect classifier has an AUC of 1, indicating that it can perfectly distinguish between positive and negative cases. A random classifier has an AUC of 0.5, indicating that its performance is no better than chance.

Comparative verification using the ROC curve involves comparing the AUC values of different models to determine which one performs better. A model with a higher AUC value is generally considered to be more accurate and reliable. In landslide susceptibility assessments, the ROC curve can be used to evaluate the performance of different models in predicting the occurrence of landslides. By comparing the AUC values of different models, researchers can identify the most effective model and determine the range of discrimination thresholds that yield the best performance. This information can be used to improve the accuracy and reliability of landslide susceptibility assessments and inform land-use planning and decision-making processes.

6 Results

Applied machine learning models were comparatively used to provide more accurate understanding regarding which model can provide more reliable data for susceptibility analysis of landslide probability in the studied region. To this end, various methods were used to identify and gather information about triggering factors. These data were enriched by 57 recorded cases of large-scale landslides in NR. The identified triggering factors can be classified as various elements. The triggering factors as well as recorded historical landsides’ spatial location and magnitude were rasterized and entered into the GIS environment as information layers; these layers are normalized and utilized to prepare landslide susceptibility maps for NR based on each target prediction procedure. The prediction procedures are described in Section 6 of this article properly. The results of the landslide susceptibility mapping for NR are illustrated in Figure 4. According to this figure, each MLP, SVM, and DT classifier provide susceptibility maps for the studied region with some differences. By comparatively looking at the maps, it will appear that the SVM and MLP are nearly close to each other. A general overview of the provided susceptibility maps can indicate that the main high-risk areas are located on the west side of the NR. These areas are related to the main faults and seismic activities in the NR, especially the south-west region. Most of the recorded historical landslides are identified in that section.

Figure 4 Comparatively prepared landslide susceptibility maps for NR: (a) MLP, (b) SVM, and (c) DT.
Figure 4

Comparatively prepared landslide susceptibility maps for NR: (a) MLP, (b) SVM, and (c) DT.

Once the model is trained and validated, it is used to predict landslide susceptibility for the entire study area. The output of the model typically consists of continuous probability values representing the likelihood of landslide occurrence. These probabilities are then classified into five susceptibility levels – very high, high, moderate, low, and very low – based on predefined thresholds or decision rules. This classification scheme enables the identification of areas with varying degrees of susceptibility to landslides. For example, in landslide susceptibility analysis, areas may be classified into five classes, ranging from very low susceptibility (indicating minimal risk of landslides) to very high susceptibility (indicating a high likelihood of landslides). The assignment of areas to susceptibility classes is typically based on the analysis of various contributing triggering factors and historical landslide occurrences. Susceptibility classes provide valuable insights into the spatial distribution of risk within a study area, enabling stakeholders to prioritize mitigation efforts, implement land use planning measures, and make informed decisions to reduce the impact of hazards on communities and infrastructure.

In this study, the MLP, SVM, and DT models are employed in landslide susceptibility analysis to define susceptibility classes and assess the likelihood of landslide occurrence in NR. In this case, predictive models are trained using historical landslide data and triggering factors. The models learns the complex relationships between these factors and landslide occurrences to predict the likelihood of landslides in different areas. By analyzing the output probabilities generated by the MLP model, susceptibility classes can be defined, ranging from low to high susceptibility, based on predefined thresholds. The SVM model aims to find the hyperplane that best separates the classes in the feature space. The distance between the hyperplane and the data points, known as the margin, is maximized to ensure robust classification. Based on the predicted probabilities or distances from the hyperplane, susceptibility classes can be defined, providing insights into the spatial distribution of landslide risk. Additionally, DT models recursively partition the feature space into subsets based on the most discriminative features, such as slope, aspect, and soil type. Each node in the DT represents a decision based on a specific feature, leading to the classification of areas into different susceptibility classes. By analyzing the decision paths and criteria used by the DT model, susceptibility classes can be defined based on the resulting tree structure and the likelihood of landslide occurrence associated with different combinations of environmental factors.

The relationship between recorded historical landslides and their distance to the high and very high susceptible zones is considerable. In such circumstances, it can be stated that the MLP and SVM models are more accurate than DT. Also, the higher frequency ratios of occurred landslide recorded to the close to the recorded landslides by the SVM and MLP are need to be notified. ROC curve was used to verify the predictive models’ performances. To evaluate the overall accuracy of the landslide susceptibility maps and the binary classification model used to create it, ROC verification can be used efficiently. ROC analysis involves plotting the TPR (sensitivity) against the FPR (1 − specificity) for different threshold values. This creates a curve that represents the performance of the model at different levels of sensitivity and specificity. The AUC can be used as a measure of the accuracy of the model. A higher AUC indicates better performance, with a value of 1 indicating perfect accuracy. By comparing the AUC of different models or different input parameters, it is possible to determine which factors are most important in predicting landslide susceptibility and which model is most accurate. Figure 5 presents the ROC curve analysis results for MLP, SVM, and DT predictive models. According to this figure, the MLP model reached the highest accuracy rate than SVM and DT. The models’ weight indexes are provided in Tables 3 and 4.

Figure 5 ROC analysis curve obtained for utilized predictive models.
Figure 5

ROC analysis curve obtained for utilized predictive models.

Table 3

Landslide triggering factors

Class Triggering factors SVM weight MLP weight DT weight
Morphologic Elevation 4 5 5
Slope aspect 5 4 4
Slope angle 3 3 4
Geologic Lithology 5 4 5
Drainage density 3 5 3
Distance to river 5 4 3
Weathering 3 5 3
Land cover‎ 5 4 4
Climatologic Precipitation 5 4 5
NDVI 4 5 3
Seismicity Distance to faults 3 4 5
Human activity Distance to roads 3 4 4
Distance to the cities 3 4 5
Table 4

Pixel comparison for output susceptibility models

Susceptibility class MLP model SVM model DT model
Pixel rate Percentage Pixel rate Percentage Pixel rate Percentage
Very low 359 12 385 10 218 8
Low 1,425 35 1,906 37 1,639 26
Moderate 1,247 21 1,054 22 2,159 35
High 963 24 636 19 756 21
Very high 165 8 105 12 210 10
High and Very high 1,128 32 741 31 966 31

In accordance with ROC results, it can be stated that the MLP model with an overall accuracy of 0.883 is classified in the highest rank for landslide susceptibility mapping. The SVM model reached 0.842 as overall accuracy, which is located in the second rank. DT predictive model provides the lowest overall accuracy among other predictive classifiers.

7 Discussion

The scientific study explores the efficacy of machine learning models in predicting landslide susceptibility within a region denoted as NR. By integrating various factors along with historical landslide data, the researchers created susceptibility maps using different predictive models like MLP, SVM, and DT. These factors were transformed into rasterized layers and processed within a GIS environment, forming the basis for the analysis. The resulting susceptibility maps exhibited distinct variations among the models employed. Notably, high-risk areas were concentrated in the western part of NR, aligning with main faults and seismic activity zones, which correlated strongly with the occurrence of historical landslides. The comparative analysis of the generated maps indicated that MLP and SVM models demonstrated closer predictions in comparison to the DT model.

To evaluate the models’ performances, the researchers utilized ROC analysis, plotting sensitivity against 1 − specificity for different threshold values. This allowed for the calculation of the AUC, a measure of model accuracy. The findings revealed that MLP achieved the highest accuracy with an AUC of 0.883, followed by SVM with an AUC of 0.842, while DT exhibited the least accuracy among the models. In conclusion, the study highlighted that MLP and SVM models showcased superior accuracy in predicting landslide susceptibility compared to DT. These findings provide crucial insights for assessing and managing landslide risks within the NR region, offering valuable guidance for future mitigation strategies and risk management protocols.

8 Conclusion

In conclusion, the landslide susceptibility analysis using MLP, SVM, and DT algorithms has shown promising results in predicting ‎the likelihood of landslides in the studied region (NR). Each of the three models has its own advantages and ‎disadvantages, and the choice of which model to use will depend on the specific requirements of the ‎study and the characteristics of the area being analyzed. In the studied region, the results of the overall ‎accuracy analysis indicated that the MLP reached the highest accuracy for susceptibility analysis ‎and mapping of landslide. The MLP model has shown high accuracy in predicting landslide ‎susceptibility, but it can be computationally intensive and requires a large amount of data for training. ‎The presented study used 70% of the primary database to train the models (i.e., MLP, SVM, and DT) and ‎the results were tested for remaining 30% used to validate the prediction process. The close prediction ‎regarding SVM and MLP might be related that the SVM is sensitive to the choice of kernel function and ‎hyperparameters, the “poly” kernel function used properly in the landslide susceptibility analysis for NR. ‎The DT model is simple to implement and interpret, but it is not as accurate as the other two models. ‎Nevertheless, the landslide susceptibility mapping for NR using these models has the potential to assist ‎in identifying areas that are at risk of landslides and guiding mitigation efforts to reduce the impact of ‎landslides on human society and the environment. Further research is needed to improve the accuracy of ‎these models and to develop more effective approaches to landslide susceptibility mapping.‎

Acknowledgments

The authors would like to thank the anonymous reviewers for providing invaluable ‎review comments and recommendations for improving the scientific level of the article.‎

  1. Funding information: This research was funded by the Key Improvement Projects of Guangdong Province (grant ‎No. 2022ZDJS048), the Shaoguan Science and Technology Plan Projects (grant No. ‎SZ2022KJ06 and 220607154531533), and the Science and Technology projects of Education ‎Government in Jiangxi province (grant No. GJJ209406, GJJ218505, and GJJ218504) and the National ‎Nature Sciences Foundation of China (grant No. 42250410321‎).‎

  2. Author contributions: Fei Teng, Yican Li and Subin Qian: Methodology, Conceptualization, Formal analysis, Investigation, Software, Data curation, Visualization, Writing – original draft. Yimin Mao and Yaser A. Nanehkaran: Methodology, Validation, Conceptualization, Investigation, Software, Supervision, Writing – review & editing.

  3. Conflict of interest: The authors declare that they have no conflicts of interest to report regarding the ‎present study.‎

  4. Data availability statement: All required data and information is available within paper.

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Received: 2024-02-03
Revised: 2024-03-31
Accepted: 2024-04-13
Published Online: 2024-05-31

© 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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  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
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  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
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  137. Erratum
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  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
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  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
https://doi.org/10.1515/geo-2022-0642
Keywords for this article
landslide susceptibility; support-vector machine; multilayer ‎perceptron; decision tree; geo-hazards
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  122. Enhancing the total-field magnetic anomaly using the normalized source strength
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  136. Essential questions in earth and geosciences according to large language models
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  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”
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  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
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  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
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  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
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