Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models

ABSTRACT Floods are one of the most devastating natural hazards, causing adverse effects on human life, well‐being, property, and the environment. The application of five machine‐learning techniques in pluvial flood susceptibility mapping was investigated using the case study of two severe storms (2...

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Autors principals:	Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi
Format:	Artigo
Idioma:	Inglês
Publicat:	Wiley 2025-06-01
Col·lecció:	Journal of Flood Risk Management
Matèries:	cubist extreme gradient boosting machine learning random forest Toronto urban flood susceptibility
Accés en línia:	https://doi.org/10.1111/jfr3.70051
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