基于径流特性的BP神经网络模型在中长期来水预报中的应用——以天津市于桥水库为例  

Application of BP neural network model based on runoff characteristics in medium and long term inflow forecasting—A case study of Yuqiao Reservoir in Tianjin City

作　　者：付佳祥 孙甲岚 李匡[3,4] 黄颖 冯晓乐 梁雅琪 FU Jiaxiang;SUN Jialan;LI Kuang;HUANG Ying;FENG Xiaole;LIANG Yaqi(Beijing Heyuan Technology Co.,Ltd.,Beijing 100083;Tianjin Water Engineering Operation and Dispatching Center,Tianjin 300074;China Institute of Water Resources and Hydropower Research,Beijing 100038;Engineering and Technology Research Center for Flood Control,Drought Mitigation and Disaster Reduction(Flood and Drought Disaster Defense Center),Ministry of Water Resources,Beijing 100038)

机构地区：[1]北京合源科技有限公司,北京100083 [2]天津市水务工程运行调度中心,天津300074 [3]中国水利水电科学研究院,北京100038 [4]水利部防洪抗旱减灾工程技术研究中心(水旱灾害防御中心),北京100038

出　　处：《中国防汛抗旱》2025年第2期19-23,共5页China Flood & Drought Management

基　　金：国家重点研发计划课题(2023YFC3209202);天津市防洪调度应急指挥平台建设项目(JZ120205A0042024)。

摘　　要：中长期径流预报是实施有效的流域水资源调度和科学管理的关键,预报因子筛选对于提高预报精度具有十分重要的意义。选用天津市于桥水库作为预报对象,对其实测径流过程进行分析,基于径流特性划分枯水期和丰水期,枯水期分为11月至翌年2月、3—6月两个时段,丰水期为7—10月,确定出影响于桥水库断面流量过程的影响因子,采用BP神经网络模型进行分段预报,并对全年径流整体进行预报与其对比。以1999—2020年逐月数据进行训练,以2021—2023年整体成果进行验证,结果显示划分枯水期和丰水期进行分段预报的决定系数(R2)较全年计算提高0.31,平均绝对百分比误差(MAPE)、优化了25.41%,相对误差(RE)降低了19.32%;2021年、2022年分段计算预报RE分别较全年计算降低了24.96%、16.30%,但2023年增加了6.38%。基于径流特性划分枯水期和丰水期进行分段预报的来水成果优于全年预报,能为流域水资源精细化调度及科学管理提供数据基础。Medium and long-term runoff forecasting is the key to the implementation of effective water resources scheduling and scientific management in the basin.The screening of forecasting factors is of great significance for improving the accuracy of forecasting.Yuqiao Reservoir in Tianjin City is selected as the forecast object,and the measured runoff process is analyzed.Based on the runoff characteristics,the dry and wet seasons are divided.The dry season is divided into two periods:from November to following February and from March to June.The wet season is from July to October.The influencing factors affecting the cross-section flow process of Yuqiao Reservoir were determined.The BP neural network model is used for segmented forecasting,and the overall annual runoff was forecasted and compared.The monthly data from 1999 to 2020 are used for training,and the overall results from 2021 to 2023 are used for verification.The results show that the R2 of the segmented forecast by dividing the dry season and the wet season is 0.31 higher than that of the whole year,the mean absolute percentage error(MAPE)is optimized by 25.41%,and the relative error(RE)is reduced by 19.32%.Compared with the annual calculation,the annual relative errors of the segmented calculation and forecast from 2021 to 2023 are increased by 24.96%,16.30%and 6.38%,respectively.The results of segmented forecast based on runoff characteristics in dry and wet seasons are better than those of annual forecasting,which can provide data basis for fine scheduling and scientific management of water resources in the basin.

关 键 词：径流特性 中长期来水预报 BP神经网络 于桥水库 天津市 

分 类 号：TV877[水利工程—水利水电工程] P338[天文地球—水文科学]