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Water Resources Management

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03-06-2024

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

Authors: Adisa Hammed Akinsoji, Bashir Adelodun, Qudus Adeyi, Rahmon Abiodun Salau, Golden Odey, Kyung Sook Choi

Published in: Water Resources Management | Issue 12/2024

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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.

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Title: Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review
Authors: Adisa Hammed Akinsoji
Bashir Adelodun
Qudus Adeyi
Rahmon Abiodun Salau
Golden Odey
Kyung Sook Choi
Publication date: 03-06-2024
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 12/2024
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-024-03885-x

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

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Springer Professional "Wirtschaft+Technik"

Optimal Exploitation of Water Resources by Using New Multi-Objective Reptile Search Algorithm (MORSA)

Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood

Enhancing Urban Drainage Infrastructure Through Implementation of Low Impact Development Techniques

Novel Perspectives on Environmental Dynamic Resilience: Adapting Urban Water Systems to a Changing Climate

Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

A Quantile Regression Approach to the Heterogeneity in Price Elasticity of Domestic Water Demand