水力发电2024，Vol.50Issue(6)：17-22,6.
基于GRU-CNN双网络输出构建BP模型的径流预测方法
A Runoff Prediction Method for Constructing BP Model Based on GRU-CNN Dual Network Outputs
摘要
Abstract
Improving the accuracy of runoff prediction is an important means to avoid flood disasters.However,the lack of known effective modeling samples during the prediction phase poses difficulties for the prediction.This paper proposes using dual network outputs to provide data reference for the prediction stage,combining the relationship between the dual network outputs and the true values in the training stage,and using quadratic multivariate modeling to achieve the runoff prediction in prediction stage.Firstly,the GRU and CNN deep learning networks are constructed to synchronously output two runoff prediction sequences.Secondly,within a known time period,a multivariate BP model between two predicted results and the measured values is constructed.Finally,based on the dual network output prediction values,the runoff prediction results are output through the determined BP model.After testing,the proposed method can provide reliable prior samples for predicting time periods,efficiently learn the relationship between network output and true values,and significantly improve prediction accuracy.关键词
洪水预报/径流预测/双网络输出/GRU/CNN/BP神经网络Key words
flood forecasting/runoff prediction/dual network output/GRU/CNN/BP neural network分类
建筑与水利引用本文复制引用
张玥,姜中清,周伊,周静姝,王宇露..基于GRU-CNN双网络输出构建BP模型的径流预测方法[J].水力发电,2024,50(6):17-22,6.基金项目
国家重点研发计划(2022YFC3202601) （2022YFC3202601）
