基于混沌识别SVM组合模型的水库径流预测分析  

Reservoir runoff prediction based on chaos recognition SVM combination model

作　　者：杨发军 YANG Fajun(Jiangxi Ganxi Civil Engineering Survey and Design Institute,Yichun Jiangxi,336000,China)

机构地区：[1]江西省赣西土木工程勘测设计院,江西宜春336000

出　　处：《江西水利科技》2025年第1期55-58,69,共5页Jiangxi Hydraulic Science & Technology

摘　　要：针对传统径流预测方法适应性差、准确度低的问题,本文提出基于混沌识别SVM组合模型的径流预测方法。以店下水库为例,构建混沌识别支持向量机组合预报模型,利用本方法与最大Lyapunov指数的混沌预测模型、ANN、AR模型三种模型进行对比分析,检验组合模型的应用效果。四种模型的评价指标结果依次为:平均相对误差12.3%<14.6%<17.8%<21.2%;确定性系数0.85、0.53、0.59、0.72;合格率90.1%>74.8%>68.9%>62.6%,因此基于混沌识别SVM组合预测模型的水库径流预测精度与可信度最高,预测效果优于其他方法。研究成果可为店下水库径流预测提供理论依据。Aiming at the problems of poor adaptability and low accuracy of traditional runoff prediction methods,a runoff prediction method based on chaos recognition SVM combination model is proposed.Taking Dianxia Reservoir as an example,a chaotic recognition support vector machine combination prediction model is constructed.This method is compared and analyzed with three models,i.e.the maximum Lyapunov exponent chaos prediction model,ANN,and AR models,to test the application effect of the combination model.The results showed that the average relative error evaluation indicator of the corresponding four models were 12.3%,14.6%,17.8%and 21.2%;The coefficient of certainty is 0.85,0.53,0.59 and 0.72,and the pass rate is 90.1%,74.8%,68.9%and 62.6%.Therefore,the SVM combination prediction model based on chaos recognition has the highest accuracy and credibility in predicting reservoir runoff,and the prediction effect is better than other methods.The research results can provide theoretical basis for predicting the runoff of the underground reservoir in the store.

关 键 词：水库 径流预测 小波分析法 混沌识别 支持向量机 

分 类 号：P338[天文地球—水文科学]