Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China

2021 ◽  
Author(s):  
Wei Xie ◽  
Wen Nie ◽  
Pooya Saffari ◽  
Luis F. Robledo ◽  
Pierre-Yves Descote ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hazard Assessment ◽  
Landslide Hazard ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Landslide Hazard Assessment

scholarly journals Landslide Hazard Assessment Based on Bayesian Optimization-Support Vector Machine in Nanping City, China

2021 ◽  
Author(s):  
Wei Xie ◽  
Wen Nie ◽  
Pooya Saffari ◽  
Luis F. Robledo ◽  
Pierre-Yves Descote ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hazard Assessment ◽  
Roc Curve ◽  
Confusion Matrix ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Bayesian Optimization ◽  
Slope Aspect ◽  
Support Vector ◽  
Landslide Hazard Assessment

Abstract Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of model hyperparameters is of great importance to the accuracy and precision of one landslide hazard assessment model. In this study, Bayesian Optimization (BO) method was used to tune the hyperparameters of Support Vector Machine (SVM) model to obtain a high accuracy landslide hazard zoning map. 1711 historical landslide disaster points were obtained as landslide inventory in a case of Nanping City landslide hazard assessment. A total of 12 factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected as landslide conditional factors. The multicollinearity diagnosis was performed on factors using the Spearman correlation coefficient. 1711 landslides and 1711 non-landslides were collected as the dataset and divided into the same number of training dataset and testing dataset. The confusion matrix and receiver operating characteristic (ROC) curve were used to verify the models. The results of confusion matrix accuracy and the area under ROC curve (AUC) showed that BO-SVM (89.53%, 97%) performed better than only SVM (84.91%, 0.93), which indicated the superiority of the proposed method during this study.

REGIONAL PROBABILISTIC LANDSLIDE HAZARD ASSESSMENT FOR THE ENGURI DAM (JVARI, GEORGIA)

2016 ◽  
Author(s):  
Maria Diletta Acciaro ◽  
◽  
John S. Gierke ◽  
Thomas Oommen
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Landslide Hazard Assessment

GIS-based landslide hazard assessment: an overview

2005 ◽  
Vol 29 (4) ◽  
pp. 548-567 ◽  
Author(s):  
Wang Huabin ◽  
Liu Gangjun ◽  
Xu Weiya ◽  
Wang Gonghui
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Integrated System ◽  
Process Models ◽  
Land Utilization ◽  
Property A ◽  
Landslide Hazard Assessment ◽  
Landslide Hazards ◽  
Digital Elevation ◽  
Slope Management

In recent years, landslide hazard assessment has played an important role in developing land utilization regulations aimed at minimizing the loss of lives and damage to property. A variety of approaches has been used in landslide assessment and these can be classified into qualitative factor overlay, statistical models, geotechnical process models, etc. However, there is little work on the satisfactory integration of these models with geographic information systems (GIS) to support slope management and landslide hazard mitigation. This paper deals with several aspects of landslide hazard assessment by presenting a focused review of GIS-based landslide hazard assessment: it starts with a framework for GIS-based assessment of landslide hazard; continues with a critical review of the state of the art in using GIS and digital elevation models (DEM) for mapping and modelling landslide hazards; and concludes with a description of an integrated system for effective landslide hazard assessment and zonation incorporating artificial intelligence and data mining technology in a GIS-based framework of knowledge discovery.

scholarly journals Discussion: On seismic landslide hazard assessment

Géotechnique ◽  
2008 ◽  
Vol 58 (10) ◽  
pp. 831-834
Author(s):  
A. D. W. Sparks
Keyword(s):  
Hazard Assessment ◽  
Landslide Hazard ◽  
Landslide Hazard Assessment ◽  
Seismic Landslide
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