Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations

In recent years, the number of floods around the world has increased. As a result, Flood Susceptibility Maps (FSMs) became vital for flood prevention, risk mitigation, and decision-making. The purpose of this study is to develop FSMs for Adana province on the Mediterranean coast of Türkiye using tre...

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Vydáno v:	Natural hazards (Dordrecht) Ročník 116; číslo 3; s. 2957 - 2991
Hlavní autoři:	Aydin, Halit Enes, Iban, Muzaffer Can
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.04.2023 Springer Nature B.V
Témata:	Additives Algorithms Anthropogenic factors Artificial intelligence Atmospheric models Civil Engineering Classifiers Comparative analysis Comparative studies Decision making Decision trees Earth and Environmental Science Earth Sciences Environmental Management Explainable artificial intelligence Flood control Flood mapping Flood predictions Flood prevention Flooding Floods Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Hydrogeology Immunoglobulins Learning algorithms Lithology Machine learning Mitigation Modelling Natural Hazards Original Paper Performance prediction Risk reduction River banks Riverbanks Rivers Slopes Vegetation LightGBM SHapley Additive exPlanations NGBoost CatBoost Flood susceptibility mapping
ISSN:	0921-030X, 1573-0840
On-line přístup:	Získat plný text
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