Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model-Reference-Cited by-同舟云学术

Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model

Published:2024-08-13 Issue: Volume: Page:
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Kumshe Umar Muhammad Mustapha,Abdulhamid Zakariya Muhammad,Mala Baba Ahmad,Muazu Tasiu,Muhammad Abdullahi Uwaisu,Sangary Ousmane,Ba Abdoul Fatakhou,Tijjani Sani,Adam Jibril Muhammad,Ali Mosaad Ali Hussein,Bello Aliyu Uthman,Bala Muhammad Muhammad

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11269-024-03937-2.pdf

Reference47 articles.

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5. Cho K, Kim Y (2022) Improving streamflow prediction in the WRF-Hydro model with LSTM networks. J Hydrol 605:127297. https://doi.org/10.1016/j.jhydrol.2021.127297