Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method - Ye, Chengming; Tang, Rong; Wei, Ruilong; Guo, Zixuan; Zhang, Huajun (Frontiers in Earth Science, 2023)
The accuracy of the landslide susceptibility mapping (LSM) driven by data is closely affected by the quality of non-landslide samples. This research proposed a method combining self-organizing-map (SOM) and one-class SVM (SOM-OCSVM) to generate more reasonable non-landslide samples. We designed two steps. First, randomly selection, SOM network, one class SVM, and SOM-OCSVM were used to respectively generate non-landslide sample datasets. Second, four ML models, namely logistic regression (LRG), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF) were used to verify the effects of four non-landslide sample datasets on LSM. In the region along Sichuan-Tibet Highway, we selected 11 conditioning factors and 1186 investigated landslides to perform the above experiments. The results show that SOM-OCSVM achieves the highest AUC (>0.94) and minimum standard deviation (<0.081) compared with other methods. Moreover, RF achieves the best performance in different datasets compared with other ML models. The above results prove that the proposed method can enhance the performance of ML models to produce more reliable LSM.