Forecast of flood disaster emergency material demand based on IACO-BP algorithm

Abstract

The frequent occurrence of various sudden natural disasters in the world has caused heavy losses to human beings. It is very important to forecast the demand of emergency materials in order to protect people's safety and property. The purpose of this study is to use IACO-BP algorithm to forecast the demand of emergency supplies in the case of flood disaster. In this study, the flood disaster situation in recent years published by the Ministry of Water Resources of China is selected as the experimental data set. The data are sorted and analyzed by a variety of theoretical comprehensive operation, qualitative and quantitative research method, analytic hierarchy process and system dynamics method. The improved ant colony optimization algorithm is used to model the emergency material demand, and the population, flood level, flood level of the disaster area are analyzed. As the network input, the material situation outputs the material demand, so as to forecast. The results show that the iteration times of IACO-BP algorithm are 11 and the running time is 3S. The fluctuation of IACO-BP algorithm is the least and the most stable among the three algorithms. The material satisfaction degree predicted by IACO-BP algorithm is improved by 15% from the original 80.9%. It is concluded that this algorithm is very accurate and efficient in the prediction of emergency material demand, which can better assist the disaster situation. This study contributes to the prediction of emergency material demand for emergency disaster.