Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model

Abstract

A rational landslide susceptibility mapping (LSM) can minimize the losses caused by landslides and enhance the efficiency of disaster prevention and reduction. At present, frequency ratio (FR), information value (IV), and certainty factor (CF) are widely used to quantify the relationships between landslides and their causative factors; however, it remains unclear which method is the most effective. Moreover, existing landslide susceptibility zoning methods lack full automation; thus, the results are full of uncertainties. To address this, the FR, IV, and CF were used to analyze the relationship between landslides and causative factors. Subsequently, three distinct sets of models were developed, namely random forest models (RF_FR, RF_IV, and RF_CF), support vector machine models (SVM_FR, SVM_IV, and SVM_CF), and logistic regression models (LR_FR, LR_IV, and LR_CF) using the analysis results as inputs. A Gaussian mixture model (GMM) was introduced as a new method for landslide susceptibility zoning, classifying the LSM into five distinct levels. An accuracy evaluation of the models and a rationality analysis of the LSM indicated that the FR is superior to the IV and CF in quantifying the relationship between landslides and causative factors. Additionally, the quantile method was employed as a comparative approach to the GMM, further validating the effectiveness of the GMM. This research contributes to more effective and efficient LSM, ultimately enhancing landslide prevention measures.

Keywords

• landslide susceptibility mapping
• frequency ratio
• machine-learning model
• Gaussian mixture model