Emergency Material Demand Prediction Based on Network Information and BP Neural Network:Taking Jishishan Ms6.2 Earthquake,Gansu in 2023 as an Example
To deal with the difficulties of obtaining timely disaster information in the early stages of post-earthquake emergency response,as well as the unknown demand for emergency supplies in different areas of the affected area,we introduced the real-time network resource information for predicting material needs combined with historical earthquake data.By crawling 112,672 multi-platform network information within 72 hours after the Jiashishan Ms6.2 earthquake in Gansu on December 18,2023,the network information is classified with the use of the BERT-CNN model.Combining with the historical earthquake data,the urgency of emergency needs is assessed by the TOPSIS method,which is then used as a new sample feature into the BP neural network to optimize the prediction of death toll in various towns and villages in Jishishan County.Finally,based on the death toll and the theory of safety stock,the demand for drinking water and tents in each township of the disaster-stricken area within 72 hours after the earthquake is predicted.As demonstrated by the experiments,the introduction of network resources can promptly reflect the disaster situation in the earthquake affected area,and could improve the timeliness,precision,and accuracy of emergency material prediction.
network resourcesnatural language processingBERT-CNNBP neural networkemergency material demand prediction
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