Flood risk prediction and modeling in Bauchi: leveraging machine learning models and explainable AI for urban resilience

Citation

Kafi, Kamil Muhammad and Ponrahono, Zakiah and Ash’aari, Zulfa Hanan and Barau, Aliyu Salisu (2025) Flood risk prediction and modeling in Bauchi: leveraging machine learning models and explainable AI for urban resilience. The Journal of Climate Change and Health, 26. art. no. 100490. pp. 1-11. ISSN 2667-2782

Abstract

Floods are amongst the most destructive weather and climate-related disasters, causing significant loss of life and property globally. Accurate flood risk prediction is crucial for improving disaster resilience and urban planning.

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Additional Metadata

Item Type:	Article
Subject:	Global and Planetary Change
Subject:	Public Health, Environmental and Occupational Health
Divisions:	Faculty of Forestry and Environment
DOI Number:	https://doi.org/10.1016/j.joclim.2025.100490
Publisher:	Elsevier BV
Keywords:	Flood risk; Prediction; Modeling; Bauchi; Machine learning; Explainable AI; Urban resilience; Disaster management; Climate change adaptation; Geographic information systems
Depositing User:	MS. HADIZAH NORDIN
Date Deposited:	09 Apr 2026 09:01
Last Modified:	09 Apr 2026 09:01
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.joclim.2025.100490
URI:	http://psasir.upm.edu.my/id/eprint/124307
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