Objectives Accurate susceptibility evaluation results can accurately prevent and control the dangers caused by landslides. Sample optimization is an important method for landslide susceptibility evaluation, which can effectively solve the problem of decision boundary offset generated by unbalanced samples and improve the accuracy of landslide susceptibility evaluation.
Methods Taking the southeast area of Wanzhou District of Chongqing, China as an example, ten influencing factors such as strata, land use and elevation were selected to construct a landslide susceptibility evaluation index system, and the relationship between landslide and the indices was quantitatively analyzed by frequency ratio method, and on this basis, deep neural network model (DNN), synthetic minority oversampling technique-DNN model (SMOTE-DNN), one-class support vector machine-DNN coupling model (OS-DNN), and OS-DNN-K-means clustering coupling model (OS-DNN-K-means) were used to evaluate landslide susceptibility.
Results The results show that the distance from the road, land use and strata are the main control factors for landslide development in the study area. The accuracy evaluation results show that OS-DNN-K-means (95.61%) and OS-DNN (91.16%) could improve the landslide prediction accuracy more effectively compared with SMOTE-DNN (87.97%) and DNN (81.40%).
Conclusions Sample optimization through mixed sampling and semi-supervised classification can effectively solve the problem of sample imbalance in the study area, and provide new technical support for spatial prediction of landslide disasters.
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