Optimization method of conditioning factors selection and combination for landslide susceptibility prediction

Landslide susceptibility prediction (LSP) is significantly affected by the uncertainty issue of landslide related conditioning factor selection. However, most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue. Targeted, this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP, and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors. An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors. Five commonly used factor selection methods, namely, the correlation analysis (CA), linear regression (LR), principal component analysis (PCA), rough set (RS) and artificial neural network (ANN), are applied to select the optimal factor combinations from the original 29 conditioning factors. The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models, such as CA-multilayer perceptron, CA-random forest. Additionally, multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of “accurate data, rich types, clear significance, feasible operation and avoiding duplication” are constructed for comparisons. Finally, the LSP uncertainties are evaluated by the accuracy, susceptibility index distribution, etc. Results show that: (1) multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models; (2) Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods. Conclusively, the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes. In contrast, a satisfied combination of conditioning factors can be constructed according to the proposed principle.