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

Published in:	Geocarto international Vol. 40; no. 1
Main Authors:	Alwathaf, Yahia, Al-Areeq, Ahmed M., Al-Masnay, Yousef A., Al-Aizari, Ali R., Al-Areeq, Nabil M.
Format:	Journal Article
Language:	English
Published:	Taylor & Francis Group 31.12.2025
Subjects:	eXtreme gradient boosting (XGBoost) Flood susceptibility mapping geographic information systems (GIS) machine learning techniques random Forest algorithm (RF)
ISSN:	1010-6049, 1752-0762
Online Access:	Get full text
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