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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Journal of Flood Risk Management
Subjects:
cubist
extreme gradient boosting
machine learning
random forest
Toronto
urban flood susceptibility
Online Access:https://doi.org/10.1111/jfr3.70051
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1111/jfr3.70051

Similar Items