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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10543-:d:1186764
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    References listed on IDEAS

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    1. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    2. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    3. Yang Lu, 2019. "Artificial intelligence: a survey on evolution, models, applications and future trends," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 1-29, January.
    4. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    5. Heather Lazrus & Rebecca E. Morss & Julie L. Demuth & Jeffrey K. Lazo & Ann Bostrom, 2016. "“Know What to Do If You Encounter a Flash Flood”: Mental Models Analysis for Improving Flash Flood Risk Communication and Public Decision Making," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 411-427, February.
    6. J. F. Rosser & D. G. Leibovici & M. J. Jackson, 2017. "Rapid flood inundation mapping using social media, remote sensing and topographic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 103-120, May.
    7. Taher M. Ghazal & Mohammad Kamrul Hasan & Muhammad Turki Alshurideh & Haitham M. Alzoubi & Munir Ahmad & Syed Shehryar Akbar & Barween Al Kurdi & Iman A. Akour, 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review," Future Internet, MDPI, vol. 13(8), pages 1-19, August.
    8. Narayan Prasad Nagendra & Gopalakrishnan Narayanamurthy & Roger Moser, 2022. "Management of humanitarian relief operations using satellite big data analytics: the case of Kerala floods," Annals of Operations Research, Springer, vol. 319(1), pages 885-910, December.
    9. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    10. Eslam Satarzadeh & Amirpouya Sarraf & Hooman Hajikandi & Mohammad Sadegh Sadeghian, 2022. "Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1355-1373, March.
    11. Cheon-Pyo Lee & Jung P Shim, 2007. "An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry," European Journal of Information Systems, Taylor & Francis Journals, vol. 16(6), pages 712-724, December.
    12. Nikunj K. Mangukiya & Ashutosh Sharma, 2022. "Flood risk mapping for the lower Narmada basin in India: a machine learning and IoT-based framework," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1285-1304, September.
    13. Michael L. Littman, 2015. "Reinforcement learning improves behaviour from evaluative feedback," Nature, Nature, vol. 521(7553), pages 445-451, May.
    14. Rob Lamb & Paige Garside & Raghav Pant & Jim W. Hall, 2019. "A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2457-2478, November.
    15. Abderahman Rejeb & John G. Keogh & Suhaiza Zailani & Horst Treiblmaier & Karim Rejeb, 2020. "Blockchain Technology in the Food Industry: A Review of Potentials, Challenges and Future Research Directions," Logistics, MDPI, vol. 4(4), pages 1-26, October.
    16. Abdus Samad Azad & Rajalingam Sokkalingam & Hanita Daud & Sajal Kumar Adhikary & Hifsa Khurshid & Siti Nur Athirah Mazlan & Muhammad Babar Ali Rabbani, 2022. "Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
    17. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    18. Hafiz Suliman Munawar & Fahim Ullah & Siddra Qayyum & Sara Imran Khan & Mohammad Mojtahedi, 2021. "UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    19. Mark S. Schwartz, 2016. "Ethical Decision-Making Theory: An Integrated Approach," Journal of Business Ethics, Springer, vol. 139(4), pages 755-776, December.
    20. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.
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