GIS-based landslide susceptibility zonation for roadside slope repair and maintenance in the Himalayan region

Roadside slope failure is a common problem in the Himalayan region as road construction activities disturb natural slopes. Therefore, landslide susceptibility zonation is necessary for roadside slope disaster management and planning development activities. In this study, we consider a 53-kin section of a major highway in Nepal where road services are suspended for several days in the monsoon season every year. A number of methods have been used for landslide susceptibility zonation. We employed a bivariate statistical approach for this study. Relevant thematic layer maps represent- ing various factors （e.g., slope, aspect, land use, lithology, drainage density, proximity to stream and proximity to road） that are related to landslide activity, have been prepared using Geographic Information System （GIS） techniques. A total of 277 landslides （covering a total of 29.90 km2） of various dimensions have been identified in the area. A landslide susceptibility map was prepared by overlaying a landslide inventory map with various parameter maps segmented into various relevant classes. The landslide susceptibility index was seg- mented into five zones, viz. very low, low, moderate, high and very high susceptibility. Landslide susceptibility zonation maps are useful tools for the efficient planning and management of roadside slope repair and maintenance tasks in the Himalayan region.     

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).     

A Hazard Assessment Method for Potential Earthquake-Induced Landslides – A Case Study in Huaxian County, Shaanxi Province

The hazard assessment of potential earthquake-induced landslides is an important aspect of the study of earthquake-induced landslides. In this study, we assessed the hazard of potential earthquake-induced landslides in Huaxian County with a new hazard assessment method. This method is based on probabilistic seismic hazard analysis and the Newmark cumulative displacement assessment model. The model considers a comprehensive suite of information, including the seismic activities and engineering geological conditions in the study area, and simulates the uncertainty of the intensity parameters of the engineering geological rock groups using the Monte Carlo method. Unlike previous assessment studies on ground motions with a given exceedance probability level, the hazard of earthquake-induced landslides obtained by the method presented in this study allows for the possibility of earthquake-induced landslides in different parts of the study area in the future. The assessment of the hazard of earthquake-induced landslides in this study showed good agreement with the historical distribution of earthquake-induced landslides. This indicates that the assessment properly reflects the macroscopic rules for the development of earthquake-induced landslides in the study area, and can provide a reference framework for the management of the risk of earthquake-induced landslides and land planning.     

Mapping potential areas for groundwater storage in the High Guir Basin(Morocco): Contribution of remote sensing and geographic information system

Identification of water potential areas in arid regions is a crucial element for the enhancement of their water resources and socio-economic development. In fact, water resources system-planning can be used to make various decisions and implement manage- ment of water resources policies. The purpose of this study is to identify groundwater sto- rage areas in the high Guir Basin by implementing Geographic Information System (GIS) and Remote Sensing methods. The required data for this study can be summarized into five critical factors: Topography (slope), lithology, rainfall, rock fracture and drainage. These critical factors have been converted by the GIS into thematic maps. For each cri- tical parameter, a coefficient with weight was attributed according to its importance. The map of potential groundwater storage areas is obtained by adding the products (coeffi- cient × weight) of the five parameters. The results show that 50% to 64% of the total area of the High Guir Basin is potentially rich in groundwater, where most of fracture systems are intensely developed. The obtained results are validated with specific yield of the aqui- fer in the study area. It is noted that there is a strong positive correlation between excel- lent groundwater potential zones with high flows of water points and it diminishes with low specific yield with poor potential zones.     

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD

Snow avalanches,which are widely and frequently developed at high elevations,seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area,in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis,and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM,DT,MLP and KNN were used to compile the susceptibility index map of snow avalanches,and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM,DT,MLP and KNN were in the range of［0,0. 964］,［0,815］,［0,0. 995］and［0,1］,respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them,the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain,most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m,while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely ...     

Assessment of Geological Security and Integrated Assessment Geo‐environmental Suitability in Worst‐hit Areas in Wenchuan Quake

Abstract: The Wenchuan earthquake in 2008 and geo-hazards triggered by the earthquake caused large injuries and deaths as well as destructive damage for infrastructures like construction, traffic and electricity. It is urgent to select relatively secure areas for townships and cities constructed in high mountainous regions with high magnitude earthquakes. This paper presents the basic thoughts, evaluation indices and evaluation methods of geological security evaluation, water and land resources security demonstration and integrated assessments of geo-environmental suitability for reconstruction in alp and ravine with high magnitude earthquakes, which are applied in the worst-hit areas (12 counties). The integrated assessment shows that: (1) located in the Longmenshan fault zone, the evaluated area is of poor regional crust stability, in which the unstable and second unstable areas account for 79% of the total; (2) the geo-hazards susceptibility is high in the evaluation area. The spots of geo-hazards triggered by earthquake are mainly distributed along the active fault zone with higher distribution in the moderate and high mountains area, in which the areas of high and moderate susceptibility zoning accounts for 40.1% of the total; (3) geological security is poor in the evaluated area, in which the area of the unsuitable construction occupies 73.1%, whereas in the suitable construction area, the areas of geological security, second security and insecurity zoning account for 8.3%, 9.3% and 9.3% of the evaluated area respectively; (4) geo-environmental suitability is poor in the evaluated area , in which the areas of suitability and basic suitability zoning account for 3.5% and 7.3% of the whole evaluation area.     

Characteristics analysis and model prediction of sea-salt water intrusion in lower reaches of the Weihe River,Shandong Province,China

Marine sedimentary strata are widely distributed in the coastal zone of the study area, and are rich in brine resources. The exploitation of underground water resources often first caused the intrusion of salt water in the marine strata. Based on the analysis of sea-salt water intrusion feature, the sea-salt water intrusion is divided into four stages: The occurrence and development stage(1976–1985), the rapid development stage(1986–1990), the slow development stage(1990–2000) and the stable development stage(2000–2015). Based on the comparative analysis of the relationship between seawater intrusion and influencing factors, this paper presents that the groundwater exploitation and the brine resources mining are the main control factors of sea-salt water intrusion. On this basis, we have established a numerical model of the sea-salt water intrusion. Using this model, we predicted the development trend of the sea-salt water intrusion. The results show that if the current development of groundwater and brine is maintained, the sea-salt water intrusion will gradually withdraw; once development of brine stops, sea-salt water will invade again. This provides the scientific basis for the rational exploitation of groundwater and the prevention of sea-salt water intrusion.     

Coalbed Methane Enrichment Regularity and Major Control Factors in the Xishanyao Formation in the Western Part of the Southern Junggar Basin

There are abundant coal and coalbed methane(CBM)resources in the Xishanyao Formation in the western region of the southern Junggar Basin,and the prospects for CBM exploration and development are promising.To promote the exploration and development of the CBM resources of the Xishanyao Formation in this area,we studied previous coalfield survey data and CBM geological exploration data.Then,we analyzed the relationships between the gas content and methane concentration vs.coal seam thickness,burial depth,coal reservoir physical characteristics,hydrogeological conditions,and roof and floor lithology.In addition,we briefly discuss the main factors influencing CBM accumulation.First,we found that the coal strata of the Xishanyao Formation in the study area are relatively simple in structure,and the coal seam has a large thickness and burial depth,as well as moderately good roof and floor conditions.The hydrogeological conditions and coal reservoir physical characteristics are also conducive to the enrichment and a high yield of CBM.We believe that the preservation of CBM resources in the study area is mainly controlled by the structure,burial depth,and hydrogeological conditions.Furthermore,on the basis of the above results,the coal seam of the Xishanyao Formation in the synclinal shaft and buried at depths of 700-1000 m should be the first considered for development.     

Backward automatic calibration for three-dimensional landslide models

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.     

A Combined Weight of Evidence and Logistic Regression Method for Susceptibility Mapping of Earthquake-induced Landslides: A Case Study of the April 20, 2013 Lushan Earthquake,China

The Ms 7.0 Lushan earthquake triggered a huge number of landslides. Landslide susceptibility mapping is of great importance. Weight of Evidence (WoE) and Logistic Regression (LR) methods have been widely used for LSM (Landslide Susceptibility Mapping). However, limitations still exist. WoE is capable of assessing the influence of different classes of each factor, but neglects the correlation between factors. LR is able to analyze the relationship among the factors while it is not capable of evaluating the influence of different classes. This paper proposes a combined method of LR and WoE for LSM, taking advantage of their individual merits and overcoming their limitations. An inventory of 1289 landslides was used: 70% were random-selected for training and the remaining for validation. 11 landslide condition factors were employed in the model and the result was validated using Receiver Operating Characteristic (ROC) curve. The results showed that the LR-WoE model had a better accuracy than the LR model, producing an area below the curve with values of 0.802 success and 0.791 predictive, higher than that of the LR model (0.715 success and 0.722 predictive). It is therefore concluded that the combined method of WoE and LR can provide a promising level of accuracy for earthquake-induced landslide susceptibility mapping.     

Landslide Hazard and Risk Assessment on the Northern Slope of Mt. Changbai, China

Landslide hazard and risk assessment on the northern slope of Mt. Changbai, a well-known tourist attraction near the North Korean-Chinese border, are assessed. This study is divided into two parts, namely, landslide hazard zonation and risk assessment. The 1992 Anbalagan and Singh method of landslide hazard zonation （LHZ） was modified and used in this area. In this way, an Associative Analysis Method was used in representative areas to get a measure for controlling factors （slope gradient, relative relief, vegetation, geology, discontinuity development, weak layer thickness and ground water）. For the membership degree of factor to slope failure, the middle range of limited values was used to calculate LHZ. Based on an estimation of the potential damage from slope failure, a reasonable risk assessment map was obtained using the relationship of potential damage and probable hazard to aid future planning and prediction and to avert loss of life.     

A Simplified Numerical Approach for the Prediction of Rainfall-Induced Retrogressive Landslides

Retrogressive landslides are common geological phenomena in mountainous areas and on onshore and offshore slopes. The impact of retrogressive landslides is different from that of other landslide types due to the phenomenon of retrogression. The hazards caused by retrogressive landslides may be increased because retrogressive landslides usually affect housing, facilities, and infrastructure located far from the original slopes. Additionally, substantial geomorphic evidence shows that the abundant supply of loose sediment in the source area of a debris flow is usually provided by retrogressive landslides that are triggered by the undercutting of water. Moreover, according to historic case studies, some large landslides are the evolution result of retrogressive landslides. Hence the ability to understand and predict the evolution of retrogressive landslides is crucial for the purpose of hazard mitigation. This paper discusses the phenomenon of a retrogressive landslide by using a model experiment and suggests a reasonably simplified numerical approach for the prediction of rainfall-induced retrogressive landslides. The simplified numerical approach, which combines the finite element method for seepage analysis, the shear strength reduction finite element method, and the analysis criterion for the retrogression and accumulation effect, is presented and used to predict the characteristics of a retrogressive landslide. The results show that this numerical approach is capable of reasonably predicting the characteristics of retrogressive landslides under rainfall infiltration, particularly the magnitude of each landslide, the position of the slip surface, and the development processes of the retrogressive landslide. Therefore, this approach is expected to be a practical method for the mitigation of damage caused by rainfall-induced retrogressive landslides.     

Spatiotemporal distribution of snow cover and its variation in the upper reaches of the Taolai River basin，Qilian Mountains北大核心CSCD

Snow is an important part of the cryosphere and plays an important role in the hydrological cycle and energy balance. Study of the spatiotemporal characteristics of snow cover and its change is the prerequisite for analyzing the formation,distribution and variation of runoff from mountains in inland river basins. In this study,we selected the upper reaches of the Taolai River basin of Qilian Mountains as the study area,used down⁃ scaling methods to obtain high-resolution snow depth data,and adopted methods of spatial statistics,sensitivity analysis and contribution separations to quantify snow cover distribution and variation influenced by terrain and the regional climate during the time period from 2002 to 2018. Results showed that basin early average snow depth ranged from 0 cm to 2. 5 cm,with variation from -0. 19 cm·a-1 to 0. 06 cm·a-1. The area of snow depth re⁃ duction during the study period accounted for 68. 30% of the total area. It was found that the snow depth increase more with altitude and less with the increase of slope. Variation of snow depth increased below 2 500 m a. s. l. and decreased above 2 500 m a. s. l. As the slope increases,it first increases and then decreases;the snow depth of each aspect decreases,especially in the northwest orientation. The sensitivity of snow depth to air tempera⁃ ture and solar radiation were found negative in general,while that of the precipitation was found positive. The precipitation in high-altitude areas has a relatively large contribution to the snow depth variation,while in the val⁃ ley areas,the contribution of temperature to snow cover is more significant. This work provides an example for the study of snow dynamics in the upper reaches of inland river watersheds,and benefits model simulation and prediction of mountain runoff and regional water management. © 2023 The Author(s).     

Relationship between the Extent of Igneous Rocks and Deep Structures as Determined by Gravitational and Magnetic Data in the South China Sea

The distribution of oil and gas resources in the South China Sea and adjacent areas is closely related to the structural pattern that helped to define the controlling effect of deep processes on oil-bearing basins.Igneous rocks can record important information from deep processes.Deep structures such as faults,basin uplift and depression,Cenozoic basement and magnetic basement are all the results of energy exchange within the earth.The study of the relationship between igneous rocks and deep structures is of great significance for the study of the South China Sea.By using the minimum curvature potential field separation technique and the correlation analysis technique of gravitational and magnetic anomalies,the fusion of gravitational and magnetic data reflecting igneous rocks can be obtained,through which the igneous rocks with high susceptibility/high density or high susceptibility/low density can be identified.In this study area,igneous rocks do not develop in the Yinggehai basin,Qiongdongnan basin,Zengmu basin and Brunei-Sabah basin whilst igneous rocks with high susceptibility/high density or high susceptibility/low density are widely-developed in other basins.In undeveloped igneous areas,faults are also undeveloped the Cenozoic thickness is greater,the magnetic basement depth is greater and the Cenozoic thickness is highly positively correlated with the magnetic basement depth.In igneously developed regions,the distribution pattern of the Qiongtai block is mainly controlled by primary faults,while the distribution of the Zhongxisha block,Xunta block and Yongshu-Taiping block is mainly controlled by secondary faults,the Cenozoic thickness having a low correlation with the depth of the magnetic basement.     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.