Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms

Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms

Floods pose a significant risk to urban areas worldwide, causing extensive damage to infrastructure, property, and human lives. The goal of this work is to improve Sana’a City, Yemen’s flood susceptibility mapping by utilizing two cutting-edge machine learning RF and XGBoost. The RF and XGBoost algo...

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Bibliographic Details
Main Authors: Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Geocarto International
Subjects:
Flood susceptibility mapping
machine learning techniques
random Forest algorithm (RF)
eXtreme gradient boosting (XGBoost)
geographic information systems (GIS)
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2025.2482707
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https://www.tandfonline.com/doi/10.1080/10106049.2025.2482707

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