Multi-Influencing Factors Landslide Susceptibility Prediction Model Based on Monte Carlo Neural Network

Abstract

Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation. Traditional methods require a long time to evaluate and rely heavily on human experience. Therefore, based on the key factors affecting landslides, this paper designs a geological disaster prediction model based on Monte Carlo neural network (MCNN). Firstly, based on the weights of evidence method, a correlation analysis was conducted on common factors affecting landslides, and several key factors that have the greatest impact on landslide disasters, including geological lithology, slope gradient, slope type, and rainfall, were identified. Then, based on the monitoring data of Lanzhou City, 18 367 data records were collected and collated to form a dataset. Subsequently, these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN. After determining the hyperparameters of the model, the training and prediction capabilities of the model were evaluated. Through comparison with several other artificial intelligence models, it was found that the prediction accuracy of the model studied in this paper reached 89%, and the Macro-Precision, Macro-Recall, and Macro-F1 indicators were also higher than other models. The area under curve (AUC) index reached 0.8755, higher than the AUC value based on a single influencing factor in traditional methods. Overall, the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.