Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy o...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 16; H. 2; S. 336
Hauptverfasser:	Al-Aizari, Ali R., Alzahrani, Hassan, AlThuwaynee, Omar F., Al-Masnay, Yousef A., Ullah, Kashif, Park, Hyuck-Jin, Al-Areeq, Nabil M., Rahman, Mahfuzur, Hazaea, Bashar Y., Liu, Xingpeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2024
Schlagworte:	Accuracy Algorithms Analysis Case studies Cities Conditioning Cyclones Datasets desert city Disaster risk Displaced persons extreme gradient boosting Flood forecasting Flood mapping Flood predictions flood susceptibility Flooding Floods Historic floods humans Machine learning Mapping Methods model uncertainty Modelling Natural disasters Neural networks overfitting prediction random forest Random variables Remote sensing Susceptibility topography Uncertainty Yemen Yemen
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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