The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management


Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences
Additional Information: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Uncontrolled Keywords: convolutional neural networks,deep learning,flood forecasting,flood management,recurrent neural networks,Computer Science (miscellaneous),Environmental Science (miscellaneous),Geography, Planning and Development,Energy Engineering and Power Technology,Hardware and Architecture,Management, Monitoring, Policy and Law,Computer Networks and Communications,Renewable Energy, Sustainability and the Environment
Publication ISSN: 2071-1050
Last Modified: 29 Feb 2024 08:22
Date Deposited: 14 Jul 2023 09:43
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Related URLs: https://www.mdp ... 050/15/13/10543 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Review article
Published Date: 2023-07
Published Online Date: 2023-07-04
Accepted Date: 2023-07-03
Authors: Kumar, Vijendra
Azamathulla, Hazi Md.
Sharma, Kul Vaibhav
Mehta, Darshan J.
Maharaj, Kiran Tota (ORCID Profile 0000-0002-2513-5185)



Version: Published Version

License: Creative Commons Attribution

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