Application of Machine Learning Algorithm in China National Geohazard Susceptibility Assessment

Abstract

Landslide susceptibility is an important activity in landslide hazard assessment. With the advancement of artificial intelligence, the machine learning algorithm is applied in the landslide susceptibility assessment, has recently gained immense attention due to the advantages of obtaining the insights of landslide events and conditioning parameters based on data mining, which is important when tackling the challenge of mapping landslide prone areas in regional scale due to the complex nonlinear correlations among landslides and parameters and uncertainties associated during parameters reclassification. Therefore, the machine learning algorithm has become a standard approach for modeling landslide susceptibility over large regions. In this study, the random forest method is applied to produce the China national landslide susceptibility mapping based on the national landslide database containing more than 300 thousand landslide events. Thirty different categories of conditioning parameters related to the development, triggering, and potentially vulnerable elements of the landslide were collected using a scale of approximately 1:1,000,000. Through the data mining process, lithology, faults, topography, soil erosion, precipitation, and human activities were found to be the top six important contribution factors to landslide susceptibility. The mapping results show the areas of four degrees of susceptibility from high to low are 101, 191, 337, and 331 thousand square kilometers, respectively. The receiver operating curve (ROC) and area under curve (AUC) value was calculated to 0.81, indicating that results are well satisfying and could guide landslide mitigation on the national level.