scholarly journals Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province

2021 ◽  
Vol 10 (10) ◽  
pp. 646
Author(s):  
Shenghua Xu ◽  
Meng Zhang ◽  
Yu Ma ◽  
Jiping Liu ◽  
Yong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hazard Assessment ◽  
Vulnerability Assessment ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Shaanxi Province ◽  
Landslide Risk ◽  
Landslide Hazard Assessment ◽  
Landslide Risk Assessment ◽  
Xianyang City

Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.

scholarly journals COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR

2017 ◽  
Vol 23 (1) ◽  
pp. 155-172
Author(s):  
Ricardo Ríos ◽  
Alexandre Ribó ◽  
Roberto Mejía ◽  
Giovanni Molina
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
El Salvador ◽  
Hazard Assessment ◽  
Slope Angle ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Landslide Hazard Assessment ◽  
San Salvador ◽  
Geostatistical Approach

This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

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.

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