Seismic landslide susceptibility assessment based on ADASYN-LDA model

Abstract

Seismic landslide susceptibility (SLS) assessment can be used to estimate the susceptibility of landslides induced by an earthquake, which has great significance for emergency measure making and land use planning to mitigate the landslide hazard and risk. At present, one of the key problems in SLS evaluation is that the sample is extremely unbalanced, that is, the number of landslide units is usually far less than that of non-landslide units, which easily leads to poor prediction performance of SLS model. In addition, when using high-resolution mapping units, the sample size greatly increases, and thus the training time cost is considerable especially for complicated nonlinear artificial intelligence such as the Support Vector Machine and the Neural Networks. In view of this, this paper proposes an SLS model (called ADASYN-LDA) combining ADASYN sample method and linear discriminant analysis (LDA), and evaluates it using 2014 Ludian earthquake event. The results show that the two ADASYN-LDA models are highly efficient in training, and have good prediction performance with the high AUC of 0.8737 and 0.8535 respectively, and with the balanced accuracy of 0.7950 and 0.6174 respectively. Compared with the traditional LDA model without ADASYN method, ADASYN-LDA model improves the results of the SLS assessment affected by the sample imbalance. The further evaluation is needed in other earthquake events, and the further improvement can be made with more landslide conditioning factors and seismic landslide data set.