Landslide Susceptibility Prediction Modeling and Interpretability Based on Self-Screening Deep Learning Model

To address the problems of landslide susceptibility prediction (LSP) modeling including possible errors in landslide and non-landslide samples, complex non-linear relationships between environmental factors and unaddressed machine learning interpretability, a deep learning-based Self-screening Bi-directional Long Short-Term Memory and Conditional Random Fields (SBiLSTM-CRF) model is proposed to reduce the impact of these problems on LSP and improve its confidence. The SBiLSTM-CRF model has the advantages of deep learning network with deep layers, wide width and iterative modeling, which can predict the non-linear relationship between environmental factors and automatically screen out the wrong landslide samples; it can select non-landslide samples from the initial low/very low landslide susceptibility zone through iterative modeling, and finally reveal the internal mechanism of the coupling of environmental factors to predict landslide susceptibility. The SBiLSTM-CRF model is used to predict landslide susceptibility in Yanchang County of China, and compared with cpLSTM-CRF, random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD) and logistic regression (LR) models. The results show that SBiLSTM-CRF overcomes the problems of sample error and complex nonlinear relationship between factors in traditional machine learning, has superior performance in modeling susceptibility than conventional machine learning, and the interpretability of the model reveals that factors such as slope, elevation and lithology control the development of mounded landslides in Yanchang County.