Generate Landslide Susceptibility Research Articles

Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness.

A cloud-based and open-source approach to generate landslide susceptibility maps

A cloud-based and open-source approach to generate landslide susceptibility maps

An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

This study proposed an interpretable model that combines Random Forest (RF), Optuna hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to achieve optimal landslide susceptibility evaluation and provide explanations in the northwest region of Yunnan Province in China. First, an inventory of 4447 landslides and 23 related factors was considered for the landslide susceptibility assessment. Subsequently, a hyperparameter-optimized RF model was developed using the Optuna framework and the training dataset to generate landslide susceptibility maps. The performance of the models were evaluated using accuracy (ACC), precision (PPV), recall (TPR), F1-score (F1), and the Area Under the Curve (AUC) based on the Receiver Operating Characteristic. Furthermore, the interpretability of the model was enhanced through the implementation of SHAP. The proposed model demonstrated outstanding performance on the test set, achieving an ACC of 0.7792, PPV of 0.7448, TPR of 0.8769, F1 of 0.8055, and an AUC of 0.8387. The interpretability analysis revealed that elevation, population density, distance from roads, and normalized difference vegetation index were the primary factors influencing landslide occurrences in the study area. This study provides a comprehensive framework for evaluating landslide susceptibility in specific regions and offers invaluable insights for the prevention and management of landslide disasters.

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models.

Landslide susceptibility mapping is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility mapping (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility mapping and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study's findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

Landslide susceptibility mapping with feature fusion transformer and machine learning classifiers incorporating displacement velocity along Karakoram highway

The Karakoram Highway (KKH) is a pivotal gateway within the framework of the China-Pakistan Economic Corridor. Nevertheless, its distinct and intricate geographical characteristics make it susceptible to recurrent landslides. As an essential tool, landslide susceptibility mapping (LSM) is significant in managing and alleviating landslides near the KKH. Moreover, landslide conditioning factors (LCFs) are crucial determinants influencing the outcomes of LSM. However, existing methods primarily rely on machine learning algorithms, which do not adequately account for the intricate spatial characteristics and patterns between LCFs and landslide occurrences. In response, this study introduces the feature fusion transformer (FFTR) module, constructed based on the foundations of the transformer framework, to fuse the spatial information features of all LCFs. Subsequently, the abstract high-level spatial features obtained are fed into diverse machine learning classifiers, including random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision trees (GBDT), categorical boosting (CatBoost), and extremely randomized trees (ET), to generate landslide susceptibility maps. Displacement velocity calculated by Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) is incorporated in the LSM. The results demonstrate that FFTR-RF achieves premium performance in the area under the curve (AUC) (94%), accuracy (87.31%), precision (87.21%), recall (88.02%), and F1-score (87.61%). Incorporating displacement velocity into LSM results predicted by models enhances the comprehensiveness of LSM. These methods will furnish early warning systems for landslide disasters along the KKH, thus aiding recommendations for mitigating landslides’ social and economic losses.

Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework

Landslide susceptibility mapping (LSM) founded on DEM is a growing research field with profound implications for human safety and infrastructure preservation. Many existing methods rely on extensive input data to enhance predictive accuracy. This paper aims to introduce a remote sensing-data-requirement framework for LSM. Our approach exclusively leverages a single ALOS PALSAR image, comprising three key steps: (1) Pre-processing, (2) derivation of explanatory variables, and (3) neural network modeling. To begin, we extracted 22 input variables from the ALOS PALSAR image. These variables played a pivotal role in developing the Neural Network (NN) predictor. The predictor structure consists of 22 variables in the input layer, 150 neurons in the hidden layer, and a single output layer. Our model was trained using 5,829 sample points, and subsequently, it was employed to generate landslide susceptibility (LS) map with 745,810 points. Based on the Overall accuracy metric, the model exhibited impressive performance accuracy, achieving 89.3% training and 82.3% testing accuracies. Additionally, it demonstrated a strong performance of 95.22% during training and 84.7% during testing according to the ROC curve. In conclusion, the implementation of our proposed method underscores its ability to develop remarkable accuracy model with remote sensing-data-requirement. This framework offers valuable insights for future progress in regions with challenging conditions and extensive data coverage. Moreover, it effectively handles data quality inconsistencies and data updating issues.

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco

ABSTRACT Landslides present a significant hazard to human life, infrastructure, and property, particularly in mountainous regions. In Morocco, these risks have garnered increased attention due to their detrimental impact. This study seeks to model landslide susceptibility using three machine learning classifiers (MLCs): Multi-Layer Perceptron (MLP), Random Forest (RF), and Adaptive Boosting Classifier (AdaBoost), and compare their performance. Initially, 144 landslide sites were identified, and thirteen factors pertaining to landslides were considered. The models’ performance was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The findings reveal that AUC values range from 68.7% for AdaBoost to 82.2% for RF. The generated landslide susceptibility maps can aid decision-makers in avoiding areas with a high susceptibility to landslides.

FR-weighted GeoDetector for landslide susceptibility and driving factors analysis

Landslide susceptibility analysis is an essential tool for landslide hazard management. Correlation analysis of the driving factors before landslide susceptibility analysis is crucial to obtain more accurate results and higher computational efficiency. This article presents an FR-weighted GeoDetector, which can, at different gridding scales, stably screen out the driving factors most relevant to historical landslides in the study area compared to the performance of the original GeoDetector. The correlation analysis result shows that the most relevant seven conditioning factors to historical landslides in the study area are: lithology, distance to road, elevation, slope, STI, SPI, and distance to faults. Four machine learning models (logistic regression [LR], random forest [RF], artificial neural network [ANN], and Xgboost) are implemented for landslide susceptibility analysis, demonstrating that such models can achieve higher accuracy with features filtered by the FR-weighted GeoDetector than with all features. The Xgboost models trained on seven and 12 features were used to generate landslide susceptibility maps. The overlay with historical landslides showed that the models trained on seven features generated a more reasonable landslide susceptibility map, proving that selecting crucial landslide conditioning factors is a better solution than using a full range of landslide conditioning factors.

Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment

Accurate landslide susceptibility maps are relevant for stakeholders to take effective measures and perform land use planning. The present research aims at using three data-driven approaches to generate landslide susceptibility map for the middle Yellow River catchment (northwest China) and comparing their performances, including the weighted information value (WIV), where the IV model was integrated with analytic hierarchy process (AHP), the support vector machine (SVM) and the random forest (RF) models. A landslide inventory map including 684 historical landslides was generated first by visual interpretation of remote sensing images combined with a field survey. A total of 14 thematic layers were applied to serve as the landslides influencing factors. The Pearson correlation coefficient analyzed the correlation among these factors, and the C5.0 decision tree algorithm determined the factor importance. The results demonstrated the correlation between every two factors were all less than 0.5. Three factors (including distance to road, distance to river, and slope) were the most important contributions to the landslide occurrences in the region, whereas five factors (including NDWI, plan curvature, profile curvature, surface roughness, and aspect) had minor importance. All the models predict that most of the historical landslides are identified in moderate and high susceptibility areas. For the prediction percentage of landslides in high susceptibility areas, both SVM and RF models exceed 70%. The RF model represented the best performance, with high susceptibility zones accounting for 21.9% and landslide numbers accounting for 90.5%. The comparison among the receiver operating characteristic curves indicated that the accuracy was higher in the RF model than in the other two models: the area under the curve (AUC) for the RF was 0.904, whereas that for the WIV and the SVM were 0.845 and 0.847, respectively. Hence, the RF was proven suitable for assessing the landslide susceptibility in the region. Current results can provide valuable references for future studies and landslide risk mitigation strategies.

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing to mitigating the social‐eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.

A comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)

The main purpose of this study was to compare the performance and validation of six machine learning models (extreme gradient boosting, random forest, artificial neural network, support vector machine, C4.5 decision tree, and naive Bayes) in landslide susceptibility modelling. The province of Rize, which has the highest rate of landslide events in Türkiye, was chosen as the study area. The conditioning factors (distance to roads, lithology, drainage density, slope, topographic wetness index (TWI), soil depth, distance to rivers, land use, NDVI, plan curvature, elevation, aspect, profile curvature) affecting the landslide were determined using the ReliefF method. A total of 516 landslides were identified for creating models, comparing performance, and validating results. The performance and validation of the models were determined by the receiver operating characteristics (ROC), sensitivity, specificity, accuracy, and kappa index. The results show that the XGBoost model outperforms the other five machine learning models in terms of accuracy and performance and is the most effective model for generating landslide susceptibility maps in Rize (Türkiye).

Chinese High Resolution Satellite Data and GIS-Based Assessment of Landslide Susceptibility along Highway G30 in Guozigou Valley Using Logistic Regression and MaxEnt Model

Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.

Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network.

Landslides are one of the most widespread natural hazards that cause damage to both property and life every year. Therefore, the landslide susceptibility evaluation is necessary for land hazard assessment and mitigation of landslide-related losses. Selecting an appropriate mapping unit is an essential step for landslide susceptibility evaluation. This study tested the back propagation (BP) neural network technique to develop a landslide susceptibility map in Qingchuan County, Sichuan Province, China. It compared the results of applying six different slope unit scales for landslide susceptibility maps obtained using hydrological analysis. We prepared a dataset comprising 973 historical landslide locations and six conditioning factors (elevation, slope degree, aspect, lithology, distance to fault lines, and distance to drainage network) to construct a geospatial database and divided the data into the training and testing datasets. We based on the BP learning algorithm to generate landslide susceptibility maps using the training dataset. We divided Qingchuan County into six different scales of slope unit: 4,401, 13,146, 39,251, 46,504, 56,570, and 69,013, then calculated the receiver operating characteristic (ROC) curve, and used the area under the curve (AUC) for the quantitative evaluation of 6 different slope unit scales of landslide susceptibility maps using the testing dataset. The verification results indicated that the evaluation generated by 56,570 slope units had the highest accuracy with a ROC curve of 0.9424. Overelaborate and rough division of slope units may not get the best evaluation results, and it is necessary to obtain the slope units most consistent with the actual situation through debugging. The results of this study will be useful for the development of landslide hazard mitigation strategies.

A multi-criteria landslide susceptibility mapping using deep multi-layer perceptron network: A case study of Srinagar-Rudraprayag region (India)

Landslide susceptibility analysis is vital for understanding precursors, associated risk, and early rescue operations. In the past, numerous attempts have been made to develop landslide susceptibility maps using various tools. This research work aims to generate landslide susceptibility maps (LSM) by developing a deep multi-layer perceptron network named as DMLP-LSM and to demonstrate it over Srinagar-Rudraprayag region, a landslide prone region recognized by the Geological Survey of India (GSI). Initially, training datasets are generated using the analytical hierarchy process (AHP) enabled multi-criteria analysis, which involves information from various landslide causative factors. In a first, one dimensional line of sight (1D LOS) velocity, one of the critical inputs and prominent indicator for landslide susceptibility, is generated by processing 60 Sentinel-1A scenes using multi-temporal interferometric synthetic aperture radar (MT-InSAR) algorithm. The 1D LOS velocity map, generally used for quantitative monitoring slow moving landslides, is used here as a thematic layer. The thematic layers and LSMs were fed to the proposed DMLP-LSM network as input and output, respectively. The developed model reached an accuracy of 91.47% with precision, specificity and ROC_AUC score as 92.11%, 95.55% and 0.9072, respectively. It further identified nearly 15 square km area as susceptible to landslide. The efficacy of the proposed model is compared with two well-known machine learning models for LSM preparation, namely support vector machine (SVM) and random forest (RF). In terms of accuracy, DMLP-LSM and RF had similar performance, but better than SVM. The velocity maps derived from MT-InSAR processing played a prominent role in the generation of LSM, supporting model training and performance. The proposed model achieved highest precision and specificity, and its performance would also improve LSM preparation when using larger datasets. The obtained results are promising, and the trained model can help in the rapid generation of LSMs.

GIS-based Landslide Susceptibility Zoning Using Multi-Criteria Decision-making Method: A Case Study in Binalood Mountains, Iran

INTRODUCTION: Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas, and cause a significant injury to and loss of human life and damage to properties and infrastructures. This study aimed to assess landslide susceptibility using the analytic hierarchy process (AHP) in Binalood Mountains, Razavi Khorasan Province, Iran. METHODS: Since the Binalood Mountains range has a high potential for landslides occurrence, the present study went through to map landslide susceptibility. To accomplish this, the AHP method was used, and then, receiver operating characteristic/area under the curves (AUCs) were prepared to evaluate the performance of the susceptibility map. Multiple data, such as lithology, distance to faults, land use, distance to roads, altitude, slope, aspect, stream power index, topographic wetness index, rainfall, distance from rivers, slope length index, and topographic location index, were considered for delineating the landslide susceptibility maps. These thematic layers were assigned suitable weights on the Saaty's scale according to their relative importance in landslide occurrence in the study area. The assigned weights of the thematic layers and their features were subsequently normalized using the AHP technique. Finally, all thematic layers were integrated by a weighted linear combination method in a geographic information system tool to generate landslide susceptibility maps. FINDINGS: The landslide susceptibility maps are split into five classes, namely very low, low, moderate, high, and very high. The results showed that the geological factor was the most important factor affecting the occurrence of landslides in the study area. Generally, 47.8% of the total area was considered high and very high-risk areas. The prediction accuracy of this map showed the values of AUC equal to 81.7% that showed the AHP model had very good accuracy. CONCLUSION: Overall, AHP is acceptable for landslide susceptibility mapping in the study area. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. The results revealed that the predicted susceptibility levels were found to be in good agreement with the past landslide occurrences. Possibly, this map can be used by the concerned authorities in disaster management planning to prepare rescue routes, service centers, and shelters

Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis

Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE) model with random forest (RF) and support vector machine (SVM) models. An inventory of 1723 rainfall-induced landslides was generated and randomly selected for training (1199; 70%) and validation (524; 30%) purposes. A set of 14 relevant factors was selected and checked for multicollinearity. These factors were first ranked using Information Gain and Chi-square feature ranking algorithms. Furthermore, Wilcoxon Signed Rank Test and One-Sample T-Test were applied to check their statistical significance. An optimum subset of 11 landslide causative factors was then used for generating landslide susceptibility maps (LSM) using hybrid SE-RF and SE-SVM models. These LSM’s were validated and compared using receiver operating characteristic (ROC) curves and performance matrices. The SE-RF performed better with training and validation accuracies of 96.93% and 88.94%, respectively, compared with the SE-SVM model with training and validation accuracies of 94.05% and 82.4%, respectively. The prediction matrices also confirmed that the SE-RF model is better and is recommended for the landslide susceptibility analysis of similar mountainous regions worldwide.

Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

Landslide is a natural phenomenon that can turn into a natural disaster. The main goal of this research was to spatial prediction of a high-risk region located in the Zagros mountains, Iran, using hybrid machine learning and metaheuristic algorithms, namely the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), the Harris hawks optimisation (HHO), and the bat algorithm (BA). The landslide occurrences were first divided into training and testing datasets with a 70/30 ratio. Fourteen landslide-related factors were considered, and the stepwise weight assessment ratio analysis (SWARA) were employed to determine the correlation between landslides and factors. After that, the hybrid models of ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were applied to generate landslide susceptibility maps (LSMs). Finally, in order to validation and comparison of the applied models, two indexes, namely mean square error (MSE) and area under the ROC curve (AUROC), were used. According to the validation results, the AUROC values for the ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were 0.849, 0.82, 0.895, and 0.865, respectively. The SVR-HHO showed the highest accuracy, with AUROC of 0.895 and lowest MSE of 0.147, and ANFIS-BA showed the least accuracy with an AUROC value of 0.82 and MSE value of 0.218. Based on the results, although four hybrid models with more than 80% accuracy can generate very good results, the SVR is superior to the ANFIS model, whereas the HHO algorithm outperformed the bat algorithm. The map generated in this study can be employed by land use planners in more efficient management.

Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

Landslide is a type of slope process causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e., the best-worst method (BWM) and the stepwise weight assessment ratio analysis (SWARA) techniques. For this purpose, the first step was to prepare a landslide inventory map, which was then divided randomly into the ratio of 70/30% for model training and validation. Thirteen conditioning factors were selected based on the previous studies and available data. In the next step, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-BWM and ANFIS-SWARA ensemble models, and then several quantitative indices such as positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root-mean-square-error, and the ROC curve were employed to appraise the predictive accuracy of each model. The results indicated that the ANFIS-BWM ensemble model (AUC = 75%, RMSE = 0.443) has better performance than ANFIS-SWARA (AUC = 73.6%, RMSE = 0.477). At the same time, the ANFIS-BWM model had the maximum sensitivity, specificity, and accuracy with values of 87.1%, 54.3%, and 40.7%, respectively. As a result, the BWM method was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon.

Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya

This study aims to delineate landslide susceptibility maps using an integrated approach of remote sensing, geographical information system (GIS), and Analytical Hierarchy Process (AHP) method, for Sikkim Himalaya, India. Multiple data as lithology, lineament density, geomorphology, slope, soil, rainfall, drainage density, and land use/land cover have been considered for delineating the landslide susceptibility maps. Spatially distributed maps/thematic layers of all the aforementioned parameters were created using remotely sensed as well as ground data in a GIS environment. These thematic layers were assigned suitable weights on the Saaty's scale according to their relative importance in landslide occurrence in the study area. The assigned weights of the thematic layers and their features were then normalized by using the AHP technique. Finally, all thematic layers were integrated by a weighted linear combination method in a GIS environment to generate landslide susceptibility maps. The delineated landslide susceptibility map in the study area was categorized as ‘Very Low (0.18%)’, ‘Low (63.34%)’, ‘Moderate (0.06%)’, ‘High (33.60%)’, and ‘Very High (2.76%)’, respectively. Area Under the Curve (AUC) curve shows the accuracy assessment of the study area based on AHP model and landslide inventory is 73%. The results of this study may be useful to identify the landslide susceptibility zones.