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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

周湛赞1,2,李亚军2,陈星宇1,陈亚林1

(1.空军勤务学院,江苏 徐州 221000;2.华东师范大学 通信与电子工程学院,上海 200241)

栏目:信息化应用 · 第 2025 年第 7 期 · 页码:133-137,144 · 2096-4706(2025)07-0133-06 · 中图分类号:TP391 · 文献标识码:A · DOI:10.19850/j.cnki.2096-4706.2025.07.025

摘要

为提高震后应急物资需求预测的准确性,文章提出了一种基于改进案例推理(CBR)算法的特征权重分配方法,并结合安全库存理论构建了震后应急物资需求预测模型。该模型以震级、震源深度、地震发生时间、人口密度、房屋倒塌数量、地震设防烈度、地震烈度7项地震灾害指标为基础,能够实现对震后各类应急物资需求量的准确预测。实验验证表明,采用博弈论-改进遗传算法(SAGA)和层次分析(AHP)算法优化权重后的预测模型,其获得的预测值平均相对误差比GA算法优化和SAGA算法优化后获得的预测值平均相对误差分别降低了89.57%和87.51%,这表明该模型能够为震后应急物资的高效调配提供有力的技术支持。

关键词

震后应急物资;需求预测;博弈论;SAGA算法;案例推理法

参考文献

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作者简介

周湛赞(1995—),男,汉族,湖南岳阳人,工程师,硕士研究生在读,研究方向:深度学习、数据处理。