Dynamic flood risk prediction in Houston: a multi-model machine learning approach

Dynamic flood risk prediction in Houston: a multi-model machine learning approach

In assessing flood susceptibility in Houston, key geographical parameters such as drainage density, slope, distance from rivers and roads, LULC, and rainfall data were analyzed using machine learning models, including Decision Trees, Random Forest, Gradient Boosting, SVM, and ANN. Performance evalua...

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Bibliographic Details
Main Authors: Shuchi Mishra, Aproorv Bajpai, Agradeep Mohanta, Biplab Banerjee, Shrishti Rajput, Sudipta Kundu
Format: Article
Language:English
Published: Taylor & Francis Group 2024-01-01
Series:Geocarto International
Subjects:
Flood risk prediction
machine learning techniques
statistical validation
urban planning
geographic information systems (GIS)
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2024.2432866
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https://www.tandfonline.com/doi/10.1080/10106049.2024.2432866

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