水力发电2024，Vol.50Issue(4)：19-25,7.
基于差分序列多尺度深度学习的径流量预测方法
A Runoff Prediction Method Based on Multi-scale Deep Learning of Differential Sequence
摘要
Abstract
Accurate forecasting of runoff is the main basis for preventing flooding accidents.Due to the strong non-stationarity of runoff data,runoff information is difficult to be fully mined by a single method,which easily leads to low prediction accuracy.In this paper,a runoff prediction method based on multi-scale deep learning of differential sequence is proposed.Firstly,the first-order differential series of runoff is calculated to provide modeling samples for model building.Secondly,aiming at the volatility of differential series,the variational mode decomposition(VMD)method is used to transform the difference series,and the multi-scale LSTM method is used to estimate the components of the transformed difference series.Finally,the runoff prediction is obtained by combining the original runoff tail data and the differential series prediction results.The results show that the proposed modeling method based on differential series can realize adaptive error correction,while the multi-scale deep learning method solves the data fluctuation characteristics,and the overall prediction performance is superior.关键词
径流量预测/差分序列/LSTM/多尺度/变分模态分解(VMD)/深度学习Key words
runoff prediction/differential sequence/LSTM,multi-scale,variational mode decomposition(VMD)/deep learning分类
天文与地球科学引用本文复制引用
张少卿,陈义浦,王世辉,沈昊,刘雨..基于差分序列多尺度深度学习的径流量预测方法[J].水力发电,2024,50(4):19-25,7.基金项目
2021年江苏省水利科技项目(2021068) （2021068）
2022年江苏省水利科技项目(2022011) （2022011）
