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

Vijendra Kumar (), Hazi Md. Azamathulla (), Kul Vaibhav Sharma, Darshan J. Mehta and Kiran Tota Maharaj
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Vijendra Kumar: Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
Hazi Md. Azamathulla: Department of Civil and Environmental Engineering, St. Augustine Campus, The University of West Indies, St. Augustine P.O. Box 331310, Trinidad and Tobago
Kul Vaibhav Sharma: Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
Darshan J. Mehta: Department of Civil Engineering, Dr. S & S. S. Ghandhy Government Engineering College, Surat 395008, Gujarat, India
Kiran Tota Maharaj: Department of Civil Engineering, School of Infrastructure & Sustainable Engineering, College of Engineering and Physical Sciences, Aston University Birmingham, Aston Triangle, Birmingham B4 7ET, UK

Sustainability, 2023, vol. 15, issue 13, 1-33

Abstract: 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.

Keywords: deep learning; flood forecasting; flood management; recurrent neural networks; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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