基于GWO-BP的震后过渡安置阶段应急物资需求预测

GWO-BP-based forecasting of emergency material demand inpost-earthquake transitional resettlement phase

为精准预测地震灾区过渡性安置阶段的物资需求量,提高应急物资筹措的效率和准确性,收集我国历史地震数据信息,确定对转移安置人口数目影响较大的因素,建立基于灰狼优化算法(GWO)和反向传播(BP)神经网络的安置人口预测模型,结合人口与应急物资间的数量关系,对震后过渡性安置阶段的物资需求量进行预测。结果表明:GWO-BP神经网络模型在预测转移安置人口方面,表现出较高的准确率和稳定性,能有效预测灾区安置人口数量,进而推算出相应的物资需求量。GWO-BP神经网络模型在震后过渡安置阶段的物资需求预测方面具有一定的有效性,能为震后应急物资的筹措决策提供参考。

In order to accurately predict the material demand in the transitional resettlement stage of earthquakes and improve the efficiency and accuracy of emergency material mobilization,the factors that have a great impact on the number of resettled population were determined based on the historical seismic data in China.A prediction model of the resettled population based on GWO-BP was established,which combined with the quantitative relationship between the population and emergency supplies,to predict the material demand in the transitional resettlement stage after the earthquake.The experimental results show that the GWO-BP neural network model exhibits high accuracy and stability in predicting the number of relocated populations,and can effectively predict the number of relocated populations in disaster areas,thereby calculating the corresponding material demand.GWO-BP neural network model has a certain application value in predicting material demand in post-earthquake transitional resettlement stage,and can provide a reference for the decision-making of emergency material procurement after the earthquake.