Risk Prediction Model of Sudden Public Safety Incidents Based on Neural Network

Abstract

Taking natural disasters as an example, this paper collects the data of natural disasters in China from 2014 to 2020, and makes a statistical analysis on the temporal and spatial distribution of natural disasters in China. Four prediction models including Bayesian regression, KNN, support vector machine and neural network are established. The natural disaster risk prediction indexes are constructed by using the direct economic loss of natural disasters and the affected population, and the regression fitting and prediction experiments are carried out. The fitting and prediction performance of each model are compared by using four quantitative evaluation indexes: mean absolute error, r2score, mean square error and adjusted R-square. The results show that natural disasters in China show obvious temporal and spatial distribution law, and the prediction performance of neural network model is the best.