Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review

Adisa Hammed Akinsoji (), Bashir Adelodun (), Qudus Adeyi (), Rahmon Abiodun Salau (), Golden Odey () and Kyung Sook Choi ()
Additional contact information
Adisa Hammed Akinsoji: Kyungpook National University
Bashir Adelodun: Kyungpook National University
Qudus Adeyi: Kyungpook National University
Rahmon Abiodun Salau: Kyungpook National University
Golden Odey: Kyungpook National University
Kyung Sook Choi: Kyungpook National University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 12, No 14, 4735-4761

Abstract: Abstract The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided.

Keywords: Flood prediction; Flood susceptibility; Machine learning; Optimization; Data sources (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03885-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:12:d:10.1007_s11269-024-03885-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03885-x

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().