基于多模型集合方法的北江流域月径流预报研究  

Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method

作　　者：钟逸轩 廖小龙[1] 全栩剑 易灵[1] 陈艳[1] 李媛媛[1] 薛娇 ZHONG Yixuan;LIAO Xiaolong;QUAN Xujian;YI Ling;CHEN Yan;LI Yuanyuan;XUE Jiao(China Water Resources Pearl River Planning,Surveying and Designing Co.,Ltd.,Guangzhou 510610,China)

机构地区：[1]中水珠江规划勘测设计有限公司,广东广州510610

出　　处：《人民珠江》2022年第6期37-44,共8页Pearl River

基　　金：中水珠江规划勘测设计有限公司科研项目(ZSZJ-KY-202101)。

摘　　要：准确的月径流预报对于水资源优化配置、流域防汛抗旱、开展水量调度、水库群发电优化等具有十分重要的作用。当前工作中常用的月径流预报方法主要有水量平衡模型、数理统计模型、人工神经网络等。研究表明,任何单个模型在月径流预报问题上都无法始终具有最佳预报效果,多模型集合方法为消除模型不确定性、提高月径流预报精度提供了有效途径。以北江流域坪石站、犁市站、横石站和石角站为研究对象,分析对比了季节性自回归模型、两参数月水量平衡模型、人工神经网络的应用效果,并基于BMA(Bayesian Model Averaging)法获取上述站点的多模型集合方案,研究结果表明多模型集合月径流预报的精度相比单模型具有明显提升,不仅具有更高的确定性系数DC(Determination Coefficient),还能有效降低水量误差MAPE(Mean Absolute Percentage Error),能够为流域调度决策提供更好的支持。The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive(SAR)model,two-parameter monthly water balance(TPMWB)model,and artificial neural network(ANN)model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging(BMA)method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient(DC)and a lower mean absolute percentage error(MAPE),and thus it can provide better support for decisions in dispatching in the basin.

关 键 词：多模型 集合预报 月径流 贝叶斯模式平均 北江 

分 类 号：P338[天文地球—水文科学]