Hazard Assessment of Debris Flow Based on Infinite Irrelevance Method and Probabilistic Neural Network Coupling Model

Abstract

Debris flow is one of the most harmful natural disasters, which seriously damages the ecological environment balance, human life safety and property loss. Therefore, it is necessary to predict and assess the hazard of debris flow. This paper proposes an effective model for predicting the hazard of debris flow, which is a coupled model based on the Infinite Irrelevance Method (IIM) and the Probabilistic Neural Network (PNN). Taking Xiuyan Manchu Autonomous County, Liaoning Province, China as an example, this paper selects 31 key debris flow trenches according to the geological data survey and the characteristics of debris flow disasters, and uses IIM to screen the frequency, watershed area, height difference, debris reserves, rainfall, lithology, slope, population density, NDVI, a total of 9 influencing factors as the sample data set of PNN, and randomly divided into a training set and test set, the ratio is 65% and 35%. Compared with the IIM-GRNN model, the evaluation results show that the accuracy rate of the IIM-PNN model is 91%, which is more excellent than the IIM-GRNN model with an accuracy rate of 82%. The IIM-PNN model can more effectively classify and predict the hazard of debris flow, and can be used in other regions with similar geological environmental characteristics to take measures to manage and prevent the recurrence of the disaster.