Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classific...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2024-10, Vol.16 (20), p.8918
Main Authors: Venegas-Quiñones, Héctor Leopoldo, García-Chevesich, Pablo, Valdés-Pineda, Rodrigo, Ferré, Ty P. A, Gupta, Hoshin, Groenendyk, Derek, Valdés, Juan B, McCray, John E, Bakkensen, Laura
Format: Article
Language:English
Subjects:
Classification
Climate change
Data collection
Data mining
Decision making
Digital mapping
Emergency preparedness
Flood forecasting
Floodplains
Floods
Land use
Landslides & mudslides
Machine learning
Mapping
Methods
Rain
Remote sensing
Risk assessment
Sustainable development
Topography
Variables
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16208918