基于多模型融合的中期径流预报  

Medium-term Runoff Forecasting Based on Information Fusion Models

作　　者：李福威 孙凯昕 丁伟[2] LI Fu-wei;SUN Kai-xin;DING Wei(Heyu Hydropower Development Company,Guodian Electric Power Development Co.,Ltd.,Benxi 117201,Liaoning Province,China;School of Hydraulic Engineering,Dalian University of Technology,Dalian 116024,Liaoning Province,China)

机构地区：[1]国电电力和禹水电开发公司,辽宁本溪117201 [2]大连理工大学水利工程学院,辽宁大连116024

出　　处：《中国农村水利水电》2023年第2期9-15,共7页China Rural Water and Hydropower

基　　金：国家重点研发计划项目(2021YFC3000205)。

摘　　要：为提高流域中期径流预报精度,提出了一种基于机器学习的多模型融合的中期径流预报方法,并应用于桓仁水库流域。首先采用BP神经网络(BP)、多元线性回归(MLR)、支持向量机(SVM)构建旬尺度的单一径流预报模型;再基于信息熵和机器学习方法对上述单一模型的结果进行融合,分别建立基于信息熵、BP神经网络、SVM的信息融合预报模型;进一步考虑融雪影响,构建春汛期旬径流预报模型。引入平均绝对误差(MAE)、均方根误差(RMSE)和预报合格率(QR)三个误差评价指标,综合评定各模型在汛期和非汛期的径流预报精度。结果表明:(1)所有模型对径流变化趋势的模拟效果相对较好,单一模型对峰值的模拟表现较差;(2)基于机器学习算法的融合模型能很好结合不同预报模型的优势,模拟精度优于各单一预报模型和基于信息熵的融合模型,共提高汛期10个旬的径流预报精度,且将6个旬的预报合格率提升至100%,预报合格率的最大提升率达到24%;(3)考虑融雪影响的旬径流预报模型在3月和4月的预报合格率均在90%以上,提高了流域的非汛期径流预报能力。研究提出的基于机器学习的信息融合预报方法可得到准确性和可靠性较高的径流预报模型,为桓仁水库径流预报工作和水资源高效管理提供数据支持和理论支撑。To improve the accuracy of the medium-term runoff forecasting of the watershed,a multi-model fusion method of medium-term runoff prediction method based on machine learning is proposed,which is applied to the Huanren Reservoir Basin.Firstly,BP neural network model,multiple linear regression model,and support vector machine model are used to forecast the ten-day runoff.The above models are merged based on information entropy,BP neural network,and SVM.Then,a ten-day runoff forecasting model for the spring flooding period that considers the snowmelt effect is constructed.Three error evaluation indicators,MAE,RMSE and QR,are introduced to evaluate the runoff forecasting accuracy of each model in the flood and non-flood periods.The results in Huanren Reservoir show that all models can accurately simulate the runoff change process,but the single forecasting models are poorly fitted in the peak section.The information fusion models based on machine learning algorithms can well combine the advantages of different forecasting models,and outperform each single forecasting model and the fusion model based on information entropy,which can improve the runoff forecast accuracy for 10 periods throughout the year and increase the forecast qualification rate to 100%for 6 periods with a maximum increment of 24%.The ten-day runoff forecasting model considering the effect of snowmelt has a passing rate of over 90%in both March and April,improving the non-flood runoff forecasting capability of the basin.The proposed information fusion prediction method based on machine learning can obtain runoff prediction models with high accuracy and reliability,providing data support and theoretical support for runoff forecasting work and efficient water resources management in the Huanren Reservoir.

关 键 词：中期径流预报 BP神经网络 多元线性回归 支持向量机 融合预报 桓仁水库 

分 类 号：TV124[水利工程—水文学及水资源]