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

Héctor Leopoldo Venegas-Quiñones (), Pablo García-Chevesich, Rodrigo Valdés-Pineda, Ty P. A. Ferré, Hoshin Gupta, Derek Groenendyk, Juan B. Valdés, John E. McCray and Laura Bakkensen
Additional contact information
Héctor Leopoldo Venegas-Quiñones: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
Pablo García-Chevesich: Department of Civil and Environmental Engineering, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
Rodrigo Valdés-Pineda: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
Ty P. A. Ferré: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
Hoshin Gupta: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
Derek Groenendyk: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
Juan B. Valdés: Hydrology and Water Resources Department, University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, USA
John E. McCray: Department of Civil and Environmental Engineering, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
Laura Bakkensen: School of Government and Public Policy, University of Arizona, 1145 S Campus Drive, Tucson, AZ 85721, USA

Sustainability, 2024, vol. 16, issue 20, 1-17

Abstract: 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.

Keywords: machine learning; flood hazard assessment; remote sensing; random forest model; flood mapping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/20/8918/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/20/8918/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:20:p:8918-:d:1498899

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().