Abstract:

In order to evaluate susceptibility of geological disasters reasonably and effectively, a multi-class SVM model with RBF kernel was established by using supervised machine learning model of SVM based on geological disaster survey data of Feiyun River basin in Wenzhou, Zhejiang Province. In geographic information system (GIS) platform, nine kinds of feature data for training were selected to analyze weight of each indicator by utilizing RF algorithm. Then, Gaussian function was used as RBF kernel function that mapped input feature data of SVM into high dimensional space for identification. In the meantime, an optimal method was put forward to calculate hyper parameters of C and γ in Gaussian function that were difficult to solve out in SVM model. After the model is learned from the training set, its evaluation index, i.e. area under curve (AUC), is up to 0.97 by using receiver operating characteristic curve (ROC) evaluation method, and the AUC of micro-multi-classification ROC is 0.87. The trained evaluation model not only guarantees accuracy of important classifications, but also avoids over-fitting.

Key words: landslide, susceptibility assessment, support vector machine (SVM), radial basis function (RBF), random forest (RF)