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A platform for research: civil engineering, architecture and urbanism
GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin
Landslide susceptibility map is an important tool for planning and management of landslide prone areas in better way. Logistic Regression (LR) based machine learning model has been successfully applied in many parts of the world for landslide susceptibility mapping. In this study, we have applied the LR model combined with GIS to create landslide susceptibility map of the basin area of Son La hydropower plant catchement, Vietnam. For this, a total of 186 landslide locations identified in the basin area were used to construct landslide inventory. In total, 12 landslide conditioning factors (elevation, aspect, slope, curvature, deep division, fault density, river density, road density, weathering crust, rainfall, aquifer and lithology) were used for training and validating the model. Various standard statistical indices including the ROC curve were used to evaluate performance of the LR model. Results show that predictive capability of model performance is very good (AUC = 0.832) in accurately mapping landslide susceptibility of the study area. Thus, it can be concluded that the LR model is a great tool in constructing a reliable landslide susceptibility map of the study area, which can be used in better landslide hazard management.
GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin
Landslide susceptibility map is an important tool for planning and management of landslide prone areas in better way. Logistic Regression (LR) based machine learning model has been successfully applied in many parts of the world for landslide susceptibility mapping. In this study, we have applied the LR model combined with GIS to create landslide susceptibility map of the basin area of Son La hydropower plant catchement, Vietnam. For this, a total of 186 landslide locations identified in the basin area were used to construct landslide inventory. In total, 12 landslide conditioning factors (elevation, aspect, slope, curvature, deep division, fault density, river density, road density, weathering crust, rainfall, aquifer and lithology) were used for training and validating the model. Various standard statistical indices including the ROC curve were used to evaluate performance of the LR model. Results show that predictive capability of model performance is very good (AUC = 0.832) in accurately mapping landslide susceptibility of the study area. Thus, it can be concluded that the LR model is a great tool in constructing a reliable landslide susceptibility map of the study area, which can be used in better landslide hazard management.
GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin
Lecture Notes in Civil Engineering
Ha-Minh, Cuong (editor) / Tang, Anh Minh (editor) / Bui, Tinh Quoc (editor) / Vu, Xuan Hong (editor) / Huynh, Dat Vu Khoa (editor) / Van Phong, Tran (author) / Dam, Nguyen Duc (author) / Trinh, Phan Trong (author) / Van Dung, Nguyen (author) / Hieu, Nguyen (author)
CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure ; Chapter: 186 ; 1841-1849
2021-10-28
9 pages
Article/Chapter (Book)
Electronic Resource
English
Presenting logistic regression-based landslide susceptibility results
| British Library Online Contents | 2018
| British Library Online Contents | 2014