scholarly journals Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

2021 ◽  
Vol 13 (21) ◽  
pp. 4280
Author(s):  
Laura Coco ◽  
Debora Macrini ◽  
Tommaso Piacentini ◽  
Marcello Buccolini
Keyword(s):  
Statistical Methods ◽  
Landslide Susceptibility ◽  
Statistical Approach ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Catchment Scale ◽  
Predisposing Factor ◽  
Susceptibility Maps ◽  
Reproducible Method ◽  
First Time

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).

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 The importance of input data on landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Gaidzik ◽  
María Teresa Ramírez-Herrera
Keyword(s):  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Input Data ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Topographic Data ◽  
Susceptibility Maps ◽  
Landslide Mass ◽  
The Impact

AbstractLandslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.

scholarly journals Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

2021 ◽  
Author(s):  
Tingyu Zhang ◽  
Huanyuan Wang ◽  
Tianqing Chen ◽  
Zenghui Sun ◽  
Tao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Landslide Susceptibility ◽  
Reference Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Support Vector ◽  
Operating Characteristics ◽  
Predisposing Factor

Abstract The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

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.

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