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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Veröffentlicht in:	Geocarto international Jg. 40; H. 1
Hauptverfasser:	Alwathaf, Yahia, Al-Areeq, Ahmed M., Al-Masnay, Yousef A., Al-Aizari, Ali R., Al-Areeq, Nabil M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Taylor & Francis Group 31.12.2025
Schlagworte:	eXtreme gradient boosting (XGBoost) Flood susceptibility mapping geographic information systems (GIS) machine learning techniques random Forest algorithm (RF)
ISSN:	1010-6049, 1752-0762
Online-Zugang:	Volltext
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