A review of hybrid deep learning applications for streamflow forecasting

Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focuse...

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Main Authors:	Ng K.W., Huang Y.F., Koo C.H., Chong K.L., El-Shafie A., Najah Ahmed A.
Other Authors:	58590894900
Format:	Review
Published:	Elsevier B.V. 2024
Subjects:	Algorithms Optimization Prediction River Runoff Supervised learning Decision making Deep learning Learning systems Stream flow Water management Forecasting: applications Hybrid forms Hybridisation ITS applications Learning models Machine learning applications On-machines Optimisations Review papers Streamflow forecasting algorithm machine learning optimization prediction river runoff streamflow Forecasting
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Institution:	Universiti Tenaga Nasional

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record_format	dspace
spelling	my.uniten.dspace-339832024-10-14T11:17:34Z A review of hybrid deep learning applications for streamflow forecasting Ng K.W. Huang Y.F. Koo C.H. Chong K.L. El-Shafie A. Najah Ahmed A. 58590894900 55807263900 57204843657 57208482172 16068189400 58136810800 Algorithms Optimization Prediction River Runoff Supervised learning Decision making Deep learning Learning systems Stream flow Water management Forecasting: applications Hybrid forms Hybridisation ITS applications Learning models Machine learning applications On-machines Optimisations Review papers Streamflow forecasting algorithm machine learning optimization prediction river runoff streamflow Forecasting Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. � 2023 Elsevier B.V. Final 2024-10-14T03:17:34Z 2024-10-14T03:17:34Z 2023 Review 10.1016/j.jhydrol.2023.130141 2-s2.0-85171451159 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171451159&doi=10.1016%2fj.jhydrol.2023.130141&partnerID=40&md5=903795ed5d7799200bb4caaccead356f https://irepository.uniten.edu.my/handle/123456789/33983 625 130141 Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Algorithms Optimization Prediction River Runoff Supervised learning Decision making Deep learning Learning systems Stream flow Water management Forecasting: applications Hybrid forms Hybridisation ITS applications Learning models Machine learning applications On-machines Optimisations Review papers Streamflow forecasting algorithm machine learning optimization prediction river runoff streamflow Forecasting
spellingShingle	Algorithms Optimization Prediction River Runoff Supervised learning Decision making Deep learning Learning systems Stream flow Water management Forecasting: applications Hybrid forms Hybridisation ITS applications Learning models Machine learning applications On-machines Optimisations Review papers Streamflow forecasting algorithm machine learning optimization prediction river runoff streamflow Forecasting Ng K.W. Huang Y.F. Koo C.H. Chong K.L. El-Shafie A. Najah Ahmed A. A review of hybrid deep learning applications for streamflow forecasting
description	Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. � 2023 Elsevier B.V.
author2	58590894900
author_facet	58590894900 Ng K.W. Huang Y.F. Koo C.H. Chong K.L. El-Shafie A. Najah Ahmed A.
format	Review
author	Ng K.W. Huang Y.F. Koo C.H. Chong K.L. El-Shafie A. Najah Ahmed A.
author_sort	Ng K.W.
title	A review of hybrid deep learning applications for streamflow forecasting
title_short	A review of hybrid deep learning applications for streamflow forecasting
title_full	A review of hybrid deep learning applications for streamflow forecasting
title_fullStr	A review of hybrid deep learning applications for streamflow forecasting
title_full_unstemmed	A review of hybrid deep learning applications for streamflow forecasting
title_sort	review of hybrid deep learning applications for streamflow forecasting
publisher	Elsevier B.V.
publishDate	2024
_version_	1814061097529901056

A review of hybrid deep learning applications for streamflow forecasting

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