DOI: 10.1007/s11069-021-04862-y
论文题名: Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
作者: Xie W. ; Nie W. ; Saffari P. ; Robledo L.F. ; Descote P.-Y. ; Jian W.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
语种: 英语
中文关键词: Bayesian optimization method
; GIS
; Landslide hazard assessment
; Machine learning
; Support vector machine
英文摘要: 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. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169471
Appears in Collections: 气候变化与战略
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作者单位: School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China; Spatial Information Technology and Big Data Mining Research Center in Southwest Petroleum University, Chengdu, 610500, China; Zhejiang Zhipu Engineering Technology Co., Ltd, Huzhou, 313002, China; Universidad Andres Bello, Santiago, 7500971, Chile; Department of Geotechnical and Geological Engineering, Fuzhou University, Fuzhou, 350108, China
Recommended Citation:
Xie W.,Nie W.,Saffari P.,et al. Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China[J]. Natural Hazards,2021-01-01