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.
Highlights
Landslides are cascading geological hazards in which rock, soil, or rock debris moves down the slope under the action of gravity (Fan et al 2019; Hungr et al 2013)
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
The results of confusion matrix accuracy and the area under receiver operating characteristic (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
Summary
Landslides are cascading geological hazards in which rock, soil, or rock debris moves down the slope under the action of gravity (Fan et al 2019; Hungr et al 2013). Landslide disasters caused a large number of casualties and economic losses worldwide (Kirschbaum et al 2009). It is unequivocal that the stability of natural and engineered slopes has been affected, resulting in a greater risk of landslides (Gariano and Guzzetti 2016). How to mitigate the serious threat of landslide disasters and prevent new landslides has become an increasingly important issue (Intrieri et al 2019). Landslide hazard assessment is an effective measure to prevent landslide hazards, which can provide key information for disaster prevention, disaster mitigation, and disaster risk reduction (van Westen et al 2008; Xu et al 2012)
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