现代信息科技2025，Vol.9Issue(7)：133-137,144,6.DOI:10.19850/j.cnki.2096-4706.2025.07.025
基于改进CBR算法特征权重分配的震后应急物资需求预测方法
Post-earthquake Emergency Material Demand Forecasting Method Based on Improved CBR Algorithm Feature Weight Distribution
周湛赞 1李亚军 2陈星宇 3陈亚林3
作者信息
- 1. 空军勤务学院,江苏 徐州 221000||华东师范大学 通信与电子工程学院,上海 200241
- 2. 华东师范大学 通信与电子工程学院,上海 200241
- 3. 空军勤务学院,江苏 徐州 221000
- 折叠
摘要
Abstract
In order to improve the accuracy of post-earthquake emergency material demand forecasting,this paper proposes a feature weight allocation method based on improved Case-Based Reasoning(CBR)algorithm,and constructs a post-earthquake emergency material demand forecasting model based on safety stock theory.The model is based on seven earthquake disaster indicators such as magnitude,focal depth,earthquake occurrence time,population density,number of house collapses,seismic fortification intensity,and seismic intensity.It can accurately forecast the demand for various types of emergency materials after the earthquake.The experimental results show that the Mean Relative Error of the forecast value obtained by the forecasting model optimized by game theory-improved genetic algorithm(SAGA)and Analytic Hierarchy Process(AHP)algorithm are 89.57%and 87.51%lower than that obtained by GA algorithm optimization and SAGA algorithm optimization,respectively.This shows that the model can provide strong technical support for the efficient allocation of post-earthquake emergency materials.关键词
震后应急物资/需求预测/博弈论/SAGA算法/案例推理法Key words
post-earthquake emergency material/demand forecasting/game theory/SAGA algorithm/Case-Based Reasoning algorithm分类
信息技术与安全科学引用本文复制引用
周湛赞,李亚军,陈星宇,陈亚林..基于改进CBR算法特征权重分配的震后应急物资需求预测方法[J].现代信息科技,2025,9(7):133-137,144,6.
