多源数据融合的深度学习径流预测模型  

Deep learning runoff prediction model based on multi-source data fusion

作　　者：周庆梓 何自立[1] 吴磊[1,2] 马孝义[1,3] ZHOU Qingzi;HE Zili;WU Lei;MA Xiaoyi(College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling 712100,China;State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Institute of Water and Soil Conservation,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Area of Ministry of Education,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学水利与建筑工程学院,陕西杨凌712100 [2]西北农林科技大学黄土高原土壤侵蚀与旱地农业国家重点实验室,陕西杨凌712100 [3]西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌712100

出　　处：《水力发电学报》2023年第5期43-52,共10页Journal of Hydroelectric Engineering

基　　金：陕西省水利科技项目(SLKJ-2013-14)。

摘　　要：为探究深度学习结合多源数据融合算法在流域径流预测中的效果,采用双向长短期记忆神经网络模型,选取汉江上游区域长序列水文气象资料及大气环流因子数据集,结合集合卡尔曼滤波数据融合算法,构建研究区域内5个流域径流预测模型并进行验证。结果表明,在相同预见期内该模型相比于传统长短期记忆神经网络模型,各项预测指标均有提高且能较好捕捉径流序列极值。采用数据融合算法加入大气环流因子数据集后,不同流域模型评价指标可进一步提升且随着预见期延长模型评价指标变化更为平稳。此预测模型可有效提升流域径流预报效果,为基于深度学习的径流预测提供参考。To explore the effect of deep learning algorithms combined with the multi-source data fusion method in watershed runoff prediction,a bidirectional Long Short-Term Memory(LSTM)neural network model and a data fusion algorithm of the ensemble Kalman filter are combined to construct runoff prediction models for five watersheds in the upper Hanjiang River.These models are verified using long-series hydrometeorological datasets from the study area and atmospheric circulation factor datasets.The results show that in the same prediction period,the models improve the prediction indexes and better capture the extreme values of runoff series in comparison with the traditional LSTM model.After the data fusion algorithm is used to join the atmospheric circulation factor datasets,the evaluation indexes of different watersheds can be further improved,and their time variations are more stable with a longer forecasting period.These prediction models are effective in improving deep learning-based runoff predictions.

关 键 词：径流预测 深度学习 双向长短期记忆神经网络 多源数据融合 集合卡尔曼滤波 

分 类 号：TV124[水利工程—水文学及水资源]