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

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

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