Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of the new model is assessed via comparison wit...

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Veröffentlicht in:	Water resources management Jg. 35; H. 13; S. 4621 - 4646
Hauptverfasser:	Janizadeh, Saeid, Vafakhah, Mehdi, Kapelan, Zoran, Dinan, Naghmeh Mobarghaee
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.10.2021 Springer Nature B.V
Schlagworte:	Additives Altitude Atmospheric Sciences Bayesian analysis Bayesian theory case studies Civil Engineering Curvature Distance drainage Drainage density Earth and Environmental Science Earth Sciences Environment Environmental risk Flood management Flooding Floods Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology/Water Resources Iran Land use Lithology Mathematical models Methods Performance prediction Probability theory Rain Rainfall Regression analysis Risk management Rivers streams Surveying surveys Susceptibility Topography Training water Watersheds Iran Bayesian Additive Regression Tree (BART) Ensemble model Regression Tree Bayesian Flood susceptibility mapping
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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