scholarly journals A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

2021 ◽  
Vol 11 (11) ◽  
pp. 4993
Author(s):  
Recep Can ◽  
Sultan Kocaman ◽  
Candan Gokceoglu
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Topographic Wetness Index ◽  
Engineering Structures ◽  
Susceptibility Maps ◽  
Prone Area ◽  
Elevation Difference ◽  
Mapping Models

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

scholarly journals The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 372 ◽  
Author(s):  
Zhongjun Ma ◽  
Shengwu Qin ◽  
Chen Cao ◽  
Jiangfeng Lv ◽  
Guangjie Li ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Northeast China ◽  
Susceptibility Mapping ◽  
Changbai Mountain ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Mountain Area ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.

scholarly journals Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China

2020 ◽  
Vol 198 ◽  
pp. 03023
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Luyao Li ◽  
Mei Yang ◽  
Yuantao Yang
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Roc Curve Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Boosted Decision Tree

Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.

scholarly journals Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 762 ◽  
Author(s):  
Renwei Li ◽  
Nianqin Wang
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Topographic Wetness Index

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

scholarly journals GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

2019 ◽  
Vol 11 (1) ◽  
pp. 708-726
Author(s):  
Zorgati Anis ◽  
Gallala Wissem ◽  
Vakhshoori Vali ◽  
Habib Smida ◽  
Gaied Mohamed Essghaier
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Satellite Image ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
North Western ◽  
Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

Landslide Susceptibility Mapping and Model Performance Assessment

2013 ◽  
Vol 353-356 ◽  
pp. 3487-3493 ◽  
Author(s):  
Chen Chao Xiao ◽  
Yuan Tian ◽  
Kang Ping Si ◽  
Ting Li
Keyword(s):  
Logistic Regression ◽  
Performance Assessment ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Model Performance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Overall Performance ◽  
Better Than

In this paper landslide susceptibility mapping and model performance assessment was conducted using three models, logistic regression, GAM, and SVM, in a study area in Shenzhen, China. Ten factors, slope angle, aspect, elevation, plan and profile curvature of the slope, lithology, NDVI, building density, the distance to the river, and the distance to the fault were selected as influencing factors for the landslide occurrences. All three models were trained and the resulting susceptibility maps were created. The performances of the three models were then assessed by AUC values through a 10-fold cross-validation. It could be concluded that in the study area GAM had the best overall performance among the three models, while SVM was better than logistic regression. Based on the derived DPR values, the optimum thresholds between stable areas and risky areas for all three models were also determined.

scholarly journals GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Trung-Hieu Tran ◽  
Nguyen Duc Dam ◽  
Fazal E. Jalal ◽  
Nadhir Al-Ansari ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Soft Computing ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Measures ◽  
Susceptibility Maps ◽  
Predicted Values ◽  
Landslide Susceptibility Maps ◽  
Computing Models

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

scholarly journals Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pawan Gautam ◽  
Tetsuya Kubota ◽  
Aril Aditian
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Area Index ◽  
Causal Factors ◽  
Causative Factors

AbstractThe main objective of this study is to understand the overall impact of earthquake in upper Indrawati Watershed, located in the high mountainous region of Nepal. Hence, we have assessed the relationship between the co-seismic landslide and underlying causative factors as well as performed landslide susceptibility mapping (LSM) to identify the landslide susceptible zone in the study area. We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river, soil type, total curvature, seismic intensity, topographic wetness index, distance to fault, and flow accumulation. The earthquake-induced landslide is clustered in Northern region of the study area, which is dominated by steep rocky slope, forested land, and low human density. Among the causal factors, 'slope' showed positive correlation for landslide occurrence. Increase in slope in the study area also escalates the landslide distribution, with highest density at 43%, landslide number at 4.34/km2, and landslide area abundance at 2.97% in a slope class (> 50°). We used logistic regression (LR) for LSM integrating with geographic information system. LR analysis depicts that land cover is the best predictor followed by slope and distance to fault with higher positive coefficient values. LSM was validated by assessing the correctly classified landslides under susceptibility categories using area under curve (AUC) and seed cell area index (SCAI). The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of the LSM approach.

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