Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau

Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau

Abstract Hydrological forecasting is of great significance to regional water resources management and reservoir operation. Climate change has increased the complexity and difficulty of hydrological forecasting. In this study, a hybrid explainable streamflow forecasting model based on CNN-LSTM-Attent...

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Bibliographic Details
Main Authors: Chuchu Zhang, Yuyan Zhou, Fan Lu, Jianwei Liu, Jiayue Zhang, Zeying Yin, Mengyi Ji, Baoqi Li
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Streamflow forecasting
Flow regime
CNN-LSTM-Attention
Interpretability
The eastern Qinghai-Tibet Plateau
Online Access:https://doi.org/10.1038/s41598-024-84810-5
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https://doi.org/10.1038/s41598-024-84810-5

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