Operational low-flow forecasting using LSTMs

Operational low-flow forecasting using LSTMs

This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and fo...

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Bibliographic Details
Main Authors: Jing Deng, Anaïs Couasnon, Ruben Dahm, Markus Hrachowitz, Klaas-Jan van Heeringen, Hans Korving, Albrecht Weerts, Riccardo Taormina
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Water
Subjects:
low flow
operational forecasting
LSTM
deep learning
Rhine River
Lobith
Online Access:https://www.frontiersin.org/articles/10.3389/frwa.2023.1332678/full

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