Authors : Ogundolie O. I.; Olabiyisi S. O.; Ganiyu R. A; Jeremiah Y. S; Ogundolie F. A.

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/44dpcynn

Scribd : https://tinyurl.com/2tu8pbna

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1770

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : River flooding is a major natural disaster that has caused enormous damage to our environment, infrastructure and human life. River flooding has led to flooding in river basins which has disrupted human activities and fatalities. This study is a review of river basin flooding, the impact of machine learning techniques in flood prediction in river basins, flood management in the past and the impact of machine learning in flood management. This review further examined how the Categorical boosting algorithm (CatBoost) which is a machine learning technique, could improve flood prediction in river basins and its applications in flood management. Several case studies of how CatBoost models have been used to predict flooding and enhance early warning systems were also reviewed in this study. CatBoost has been recognized to be excellent in working on categorical variables making it efficient in handling datasets with complex relationships. This makes it applicable for flood prediction in river basins considering the factors involved in flooding. CatBoost's effectiveness in flood forecasting and flood susceptibility modelling was demonstrated in some case studies. CatBoost has the potential to change flood management, minimize the disastrous impacts of floods, and enhance sustainable development, regardless of its limits. The review highlights the importance of machine learning to improve flood protection and the need for concerted efforts to get beyond implementation obstacles and take full advantage of CatBoost's flood management capabilities.

Keywords : Flooding, CatBoost, Flood Management, Machine Learning.

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