Susceptibility analysis of geological hazards based on the random forest weighted information value model: A case study of Shidian County,Yunnan Province

Traditional information value models for evaluating geological hazard susceptibility typically involve simply summing the information values of various evaluation factors, without considering the differences in weight among these factors. This can affect the scientific rigor and rationality of susceptibility zoning to some extent. To address this issue, this paper takes Shidian County of Yunnan Province as an example and introduces the random forest model to calculate the weights of each evaluation factor. After constructing an appropriate evaluation index system, the information value and weight of each factor are calculated individually, followed by a weighted summation. According to the equal interval classification method, the study area is then divided into four susceptibility levels--extremely high, high, medium, and low. To verify the accuracy of the model, the latest geological hazard hidden points identified through detailed investigations and risk assessments over the past 3 a were overlaid with the susceptibility zones. The accuracy was analyzed through hazard point density analysis and ROC curve comparison. Based on the comparison of research results, after introducing the random forest weighting, the density of extremely high-risk hidden hazard points increased from 1.754 to 1.926, and the AUC value improved from 0.809 to 0.847. The research results indicate that introducing random forest for weighting in a single information quantity model can effectively reflects the weight differences among factors, enhancing the precision of geological disaster susceptibility zoning. This method shows higher accuracy in practical applications.