摘要
精准、高效地预测地震灾害中的应急物资需求,对提升救援工作的精准性和效率至关重要。文章采用间接预测方法,通过核主成分分析对预测指标进行降维,选取主成分作为长短期记忆神经网络模型的输入变量。同时,利用改进的粒子群优化算法对长短期记忆神经网络模型的单元数和批处理大小进行优化,从而构建伤亡人数预测模型。此外,将预测的伤亡人数与安全库存理论相结合,建立应急物资需求预测模型。基于震级在6级以上的地震数据,构建的伤亡人数预测模型在均方误差、均方根误差和平均绝对误差等评估指标上表现优异。与改进的灰色预测模型[GM(1,1)]、粒子群优化算法与反向传播神经网络(PSO-BP)模型及卷积神经网络(CNN)模型相比,其误差分别降低了71%~97%、46%~83%和34%~62%。以2019年四川省宜宾市长宁县6级地震和2020年新疆喀什地区伽师县6.4级地震为案例,精准预测两地的伤亡人数及各类应急物资的需求量。该方法为提高地震灾害管理及救援工作的效率和响应能力提供了新的技术支持,具有重要的应用价值。
Accurate and efficient prediction of emergency material demand during earthquakes is crucial for improving the flexibility and efficiency of rescue operations.This study proposes an indirect prediction approach involving dimensionality reduction of prediction indicators via kernel principal component analysis,using the resulting principal components as input variables to a long short-term memory(LSTM)neural network.An improved particle swarm optimization algorithm was employed to optimize the number of LSTM units and batch size,enabling the development of a casualty prediction model.The predicted casualties were then integrated with safety stock theory to establish an emergency material demand prediction model.Using earthquake data with magnitudes≥6,the casualty prediction model outperformed others in evaluation metrics such as mean square error,root mean square error,and mean absolute error,reducing errors by 71%—97%,46%—83%,and 34%—62%compared to the improved GM(1,1),particle swarm optimization-back propagation,and convolutional neural network models,respectively.Case studies of the 2019 M 6.0 earthquake in Changning County(Yibin,Sichuan)and the 2020 M 6.4 earthquake in Payzawat County(Kashgar,Xinjiang)validated the precision of the model in predicting casualties and material demands.The proposed method provides novel technical support for enhancing earthquake disaster management and rescue efficiency,offering remarkable practical value.
作者
王国保
蔡水涌
杨红刚
谢本凯
WANG Guobao;CAI Shuiyong;YANG Honggang;XIE Benkai(Zhengzhou University of Aeronautics,Zhengzhou 450046,Henan,China;AVIC Jonhon Optronic Technology Co.,Ltd.,Luoyang 471000,Henan,China)
出处
《地震工程学报》
北大核心
2025年第4期925-936,共12页
China Earthquake Engineering Journal
基金
河南省软科学研究计划项目(212400410099)
河南省高等教育教学改革研究与实践项目(2021SJGLX470)。
关键词
地震灾害
应急物资
需求预测
改进粒子群优化算法
长短期记忆神经网络
earthquake disaster
emergency materials
demand prediction
improved particle swarm optimization(IPSO)algorithm
long short-term memory(LSTM)neural network
