Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios

Abstract Predicting runoff with precision holds immense importance for flood control, water resource management, and basin ecological dispatch. Deep learning, especially long short-term memory (LSTM) neural networks, has excelled in runoff prediction, often outperforming traditional hydrological mod...

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Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Miao He, Xian Xu, Shaofei Wu, Chuanxiong Kang, Binbin Huang
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-02-01
Subjects:	Runoff prediction Reginal modelling CAMELS Informer Transformer LSTM
Online Access:	https://doi.org/10.1038/s41598-025-89837-w