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.     

Flooding and its relationship with land cover change,population growth,and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.     

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.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Mapping horizontal displacement of soil nail walls using machine learning approaches

Machine learning (ML) approaches have stormed nearly all engineering fields since recent years. However, the situation is somehow subtle in civil engineering practice, especially in the sub-field of geotechnical engineering where data from real-life projects are usually scarce, which in turn prevents development of meaningful mapping tools based on ML techniques. This study first shares a database containing a total of 376 measured horizontal displacements from instrumented soil nail walls reported in the literature. Then, these data are utilized to develop three types of ML models for mapping the wall horizontal displacement along depth, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The uncertainties of the ANN, RF, and SVM models are then quantitatively evaluated using bias statistics where bias is defined as the ratio of measured to predicted horizontal displacement. The three ML models are proved to be accurate on average with medium dispersions in prediction, which outperform the existing simple empirical regression models. Probability distribution functions for those biases are also characterized. This study demonstrates that introduction of machine learning approaches into the reliability-based design framework for soil nail walls and other geotechnical structures is promising.

     

Modelling of shallow landslides with machine learning algorithms

This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency.     

Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.     

基于机器学习模型的淮河流域中长期径流预报研究

准确可靠的中长期径流预报是支撑水资源科学调配、提高水资源利用效率的关键。本研究采用AdaBoost模型(AdB)、随机森林模型(RF)和支持向量机模型(SVM)进行淮河流域王家坝和蚌埠站当年11月至次年10月共12个月的中长期径流预报研究。采用置换准确度重要性度量法从130项气象-气候因子及前期降雨/流量构建的1 562个因子变量中筛选出影响各月径流的关键因子,构建了基于AdB、RF和SVM模型的各月径流预报模型,模型参数采用随机搜索技术并结合交叉验证方式确定。采用变幅误差合格率和等级(五级)预报合格率指标对模型的预报精度进行了评估。变幅误差合格率指标表明,王家坝12个月的平均合格率分别为99.8%(AdB)、96.6%(RF)和95.9%(SVM),蚌埠站分别为100%(AdB)、94.8%(RF)和93.8%(SVM);等级预报合格率指标表明,王家坝12个月的平均合格率分别为79.0%(AdB)、76.4%(RF)和79.9%(SVM),蚌埠站分别为81.0%(AdB)、75.6%(RF)和76.6%(SVM)。模型均具有较好的预报效果,但RF和SVM模型对于高流量值的预报存在偏低现象,AdB模型整体上优于RF和SVM模型。     

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.     

山洪预报预警技术研究进展

山洪预报预警研究是实施山洪灾害防治的重要科学支撑。目前国内外学者对山洪预报预警技术的研究主要集中在山洪预警指标、山洪预报预警模型以及山洪灾害风险分析等方面,本文对山洪预报预警技术相关研究进展进行了总结,并指出未来在山洪灾害监测、山洪预报预警模型研制、多元信息融合技术、结合山洪特点和区划有针对性进行预报预警等方面还应进行更深入的研究。     

随机森林与GIS的泥石流易发性及可靠性

目前基于GIS的泥石流易发性（简称DFS）评价模型中,统计类型模型的因子须保证独立性,且权重受区间划分控制;线性机器学习难以处理非线性问题、而常用非线性模型调试效率低.鉴于随机森林（RF）能有效克服常用模型的诸多不足,且在DFS评价中的应用极少,首先展开基于RF的DFS评价,采用线性、RBF支持向量机、二次判别分析、RF等经贝叶斯优化的模型和26种泥石流影响因子;然后,分别以RF的相对权重排序和蒙特卡洛方法研究因子组合和建模样本变化下DFS评价的可靠性.结果表明:RF不易发和较易发区中有21个因子可指示泥石流孕育环境差异;RF的相对权重排序能有效确定易发模型的局部最优因子组合;随机样本划分导致的评价不确定性在中易发区最大,应通过提高建模样本比例和改善模型降低;RF的预测能力指标AUC为0.86、全局预测精度为0.79、F1分数为0.66、brier分数为0.14,以及它们的可靠度最优,可作为DFS定量评估的优先选择.      

Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping

Landslides are recognized as one of the most important natural hazards in many areas throughout the world. Producing landslide susceptibility maps have received particular attention from a wide range of scientists. The main objective of this study was to produce landslide susceptibility maps using hybrid wavelet packet-statistical models (WP-SM). In the first step, landslide susceptibility maps were produced using single artificial neural network (ANN), support vector machine (SVM), maximum entropy (MaxEnt), and generalized linear model (GLM). In the next step, the input maps were preprocessed using different mother wavelets in different levels. Then, the hybrid models were developed using the wavelet-based preprocessed maps. Results showed that the wavelet packet transform can be effectively used to produce precise landslide susceptibility maps. It was shown that wavelet packet transform significantly enhanced the ability of the single statistical models. The kappa coefficients were increased from 0.829 to 0.941, 0.846 to 0.978, 0.744 to 0.829, and 0.735 to 0.817 in hybrid ANN, SVM, MaxEnt, and GLM, respectively. The best wavelet transform was performed using bior1.5 with a three-level decomposition. It was also recognized that MaxEnt and GLM produced approximately poor results. However, SVM performed better than the other three models both in single and hybrid forms. ANN also outperformed MaxEnt and GLM models. Spatial distribution of the susceptible area is consistent with the observed landslide distribution pattern particularly in maps obtained from the hybrid models. The produced maps showed that the general pattern of susceptible area intensively followed the pattern of roads and sensitive geological formations.     

基于灰色关联度模型的区域滑坡敏感性评价

数理统计和机器学习模型如支持向量机(support vector machine,SVM)等,在区域滑坡敏感性评价中得到广泛的应用.但这些模型的建模过程往往较复杂,如在对机器学习进行训练和测试时难以选取合理的非滑坡栅格单元,而且有较多的模型参数需要确定.为提高滑坡敏感性评价建模的效率和精度,提出基于灰色关联度的敏感性评价模型.灰色关联度模型能有效计算各比较样本与参考样本之间的定量的关联度,具有建模过程简洁和评价精度高的优点,该模型目前在区域滑坡敏感性评价中的应用还没有引起研究人员的足够关注且有待进一步拓展.拟将灰色关联度模型用于浙江省飞云江流域南田—雅梅图幅(南田地区)的滑坡敏感性评价,并将得到的评价结果与SVM模型的敏感性评价结果作对比分析.结果显示,灰色关联度模型在高和极高敏感区的滑坡预测精度优于SVM模型,而在中等敏感区的滑坡预测精度略低于SVM模型;整体而言,灰色关联度模型对整个南田地区滑坡敏感性分布的预测精度略高于SVM模型.对两个模型建模过程的对比结果显示,灰色关联度模型建模较简单,具有比SVM模型更高的建模效率,为滑坡敏感性评价提供了一种新思路.     

Precipitation pattern modeling using cross-station perception: regional investigation

Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.     

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.     

Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy

The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km² characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area,Antalya, Turkey

The aim of this study is to make a comparison of the performances of two machine-learning algorithms that support vector machine (SVM) and random forest (RF) for landslide susceptibility mapping. The study makes use of a sampling strategy called two-level random sampling (2LRS). During landslide susceptibility mapping, training and testing samples must be collected from different landslide seed cells, which are then put through a fully independent sampling using the 2LRS algorithm. This approach requires fewer samples for the improvement of the computation time of both machine-learning classifications. The proposed approach was tested in the Alakir catchment area (Western Antalya, Turkey) which features numerous active deep-seated rotational landslides. In order to compare the performance of the machine-learning algorithms, three random sets were generated for SVM and three random sets generated for 10, 100, 1000 and 10,000-tree size RF. A total of 15 models were generated for comparison, and their spatial performances were performed by the area under the receiver-operating characteristic curves, which ranged between 0.82 and 0.87. The highest and lowest performances were recorded from two models in SVM and two models from the 1000-tree and 10,000-tree sized RF, respectively. These results were confirmed the landslide happened just after producing the susceptibility maps in the field.