Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

Abstract

In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors. The original conditioning factors are firstly used to construct original factors-based LSP models, and then different random errors of 5%, 10%, 15% and 20% are added to these original factors for constructing relevant errors-based LSP models. Secondly, low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method. Thirdly, the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case. Three typical machine learning models, i.e. multilayer perceptron (MLP), support vector machine (SVM) and random forest (RF), are selected as LSP models. Finally, the LSP uncertainties are discussed and results show that: (1) The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties. (2) With the proportions of random errors increasing from 5% to 20%, the LSP uncertainty increases continuously. (3) The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors. (4) The influence degrees of two uncertainty issues, machine learning models and different proportions of random errors, on the LSP modeling are large and basically the same. (5) The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide susceptibility. In conclusion, greater proportion of random errors in conditioning factors results in higher LSP uncertainty, and low-pass filter can effectively reduce these random errors.

Keywords

• Landslide susceptibility prediction
• Conditioning factor errors
• Low-pass filter method
• Machine learning models
• Interpretability analysis