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
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Zusammenfassung: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 with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the location’s altitude and distance from the river are the most important variables for assessing flooding susceptibility.
Bibliographie:ObjectType-Article-1
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-021-02972-7