基于多因子多模式集成的中长期径流预测模型  

Medium and long-term runoff prediction model based on multi-factor and multi-model integration

作　　者：陈娟[1] 徐琦 曹端祥 李国智 钟平安[1] CHEN Juan;XU Qi;CAO Duanxiang;LI Guozhi;ZHONG Ping′an(College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)

机构地区：[1]河海大学水文水资源学院,江苏南京210029

出　　处：《水科学进展》2024年第3期408-419,共12页Advances in Water Science

基　　金：国家重点研发计划资助项目(2022YFC3202801);国家自然科学基金资助项目(51909062)。

摘　　要：提高中长期径流预测精度对于水资源调度等具有重要意义和应用价值。基于国家气候中心的130项气候因子,采用皮尔逊相关系数、最大信息系数、方差增量指标筛选主要预测因子,建立基于DS(Dempster-Shafer)证据理论的多因子综合方法;采用随机森林、BP神经网络和贝叶斯网络等建立基于水文-气象因子遥相关的中长期径流预测模型,构建基于DS证据理论的预测结果集成模型。以三峡水库为对象开展实例研究,结果表明:引入遥相关因子能有效提高预测精度;基于DS证据理论的多因子综合方法能筛选出综合性更强、稳定性更优的因子,弥补单一筛选方法的不足;基于DS证据理论的多因子多模式集成方法在径流预测精度上优于单一方法单一模型,确定性系数提高到0.823,平均相对误差降低到23.2%。Improving medium and long-term runoff prediction accuracy is vital for optimal water resource operation.Based on the 130 climate factors obtained from the National Climate Center of China,the Pearson′s correlation coefficient,maximum information coefficient,and variance increment index are used to screen the main factors for runoff prediction.Then,a multifactor synthesis method based on the Dempster-Shafer(DS)evidence theory is proposed.The random forest,BP neural network,and Bayesian network are used to establish medium and long-term runoff prediction models using the screened hydrometeorological teleconnection factors.Finally,an integration model for the runoff prediction results is proposed based on the DS evidence theory.Considering the Three Gorges Reservoir as the case study,the results show that the use of hydrometeorological teleconnection factors can effectively improve prediction accuracy.Moreover,the multifactor synthesis method based on the DS evidence theory can screen the factors with better synthesis and stability,thereby mitigating the shortcomings of single-screening methods.The multifactor and multimode integration model based on the DS evidence theory has higher runoff prediction accuracy than the single-screening models,with the certainty coefficient increased to 0.823 and the average relative error reduced to 23.2%.

关 键 词：中长期径流预测 DS证据理论 随机森林 贝叶斯网络 BP神经网络 遥相关 

分 类 号：TV124[水利工程—水文学及水资源]