An information quantity and machine learning integrated model for landslide susceptibility mapping in Jiuzhaigou, China

Landslide susceptibility mapping (LSM) with machine learning (ML) models highly depends on the number and accuracy of landslides (positive samples) and non-landslides (negative samples). However, there is no existing standard method for selecting non-landslides, leading to the accuracy of negative samples being challenging to guarantee in previous studies, which leads to the loss of accuracy and reliability of the LSM model. To solve this problem, an information quantity and machine learning integrated model (IQ-ML) is proposed in this paper. The information quantity (IQ) model was introduced to preliminarily determine areas of low and very low landslide susceptibility applicable to non-landslides selection. Then, ML is used to accomplish LSM, with the support of randomly selected non-landslides. For validation purposes, the Jiuzhaigou area was selected as a case study area, three IQ-ML models (IQ-SVM, IQ-RF, and IQ-BPNN) were constructed successively for LSM and cross-validation, and further comparative analysis was conducted with three ML models (SVM, RF, and BPNN) that based on randomly selected non-landslides outside the landslide buffer zone. Finally, the ROC curve was used to evaluate each model’s prediction accuracy objectively. The experimental results show that the IQ-ML model proposed in this paper has higher prediction accuracy than the ML model. The AUC of IQ-SVM, IQ-RF, and IQ-BPNN models are 0.986, 0.993, and 0.991, respectively, which are higher than the SVM, RF, and BPNN models. The above result proves that the accurate non-landslide negative samples selected by the IQ model help to improve the accuracy and reliability of ML-based LSM.