Post-earthquake Emergency Supplies Prediction Based on BP Neural Network

Abstract

The prediction of earthquake casualty population is a typical complex prediction system, which needs to comprehensively consider a variety of factors such as the earthquake damage itself, the population distribution in the affected area and its environment. Aiming at the prediction of emergency supplies in earthquake disasters, this paper collects historical earthquake data, including key factors such as magnitude, depth of epicenter, population density of the affected area, constructs the input layer of BP neural network, and utilizes the self-learning ability of the network for training to realize the prediction of the number of people injured in an earthquake, which in turn indirectly predicts the demand for emergency supplies. The experimental results show that the prediction model of the number of earthquake injuries based on BP neural network has excellent performance in prediction accuracy, and the operational coefficient of determination of the model R^2 reaches 0.93337, which indicates that the prediction results of the model have ahigh degree of correlation and accuracy with the actual values, and it is able to provide a more accurate and reliable reference for the rescue work of the emergency supplies after the earthquake.