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

Wei Xie, Wen Nie (), Pooya Saffari, Luis F. Robledo, Pierre-Yves Descote and Wenbin Jian
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Wei Xie: Jiangxi University of Science and Technology
Wen Nie: Jiangxi University of Science and Technology
Pooya Saffari: Spatial Information Technology and Big Data Mining Research Center in Southwest Petroleum University
Luis F. Robledo: Universidad Andres Bello
Pierre-Yves Descote: Universidad Andres Bello
Wenbin Jian: Fuzhou University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 109, issue 1, No 39, 948 pages

Abstract: Abstract Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.

Keywords: Landslide hazard assessment; Bayesian optimization method; Support vector machine; GIS; Machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11069-021-04862-y

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