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

Author

Listed:
  • 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
    Intergubernmental Hydrological Programme, United Nations Educational, Scientific, and Cultural Organization, Montevideo 11200, Uruguay)

  • 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)

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.

Suggested Citation

  • Héctor Leopoldo Venegas-Quiñones & Pablo García-Chevesich & Rodrigo Valdés-Pineda & Ty P. A. Ferré & Hoshin Gupta & Derek Groenendyk & Juan B. Valdés & John E. McCray & Laura Bakkensen, 2024. "Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas," Sustainability, MDPI, vol. 16(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:8918-:d:1498899
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    References listed on IDEAS

    as
    1. Francesco Dottori & Wojciech Szewczyk & Juan-Carlos Ciscar & Fang Zhao & Lorenzo Alfieri & Yukiko Hirabayashi & Alessandra Bianchi & Ignazio Mongelli & Katja Frieler & Richard A. Betts & Luc Feyen, 2018. "Author Correction: Increased human and economic losses from river flooding with anthropogenic warming," Nature Climate Change, Nature, vol. 8(11), pages 1021-1021, November.
    2. B. Tellman & J. A. Sullivan & C. Kuhn & A. J. Kettner & C. S. Doyle & G. R. Brakenridge & T. A. Erickson & D. A. Slayback, 2021. "Satellite imaging reveals increased proportion of population exposed to floods," Nature, Nature, vol. 596(7870), pages 80-86, August.
    3. Francesco Dottori & Wojciech Szewczyk & Juan-Carlos Ciscar & Fang Zhao & Lorenzo Alfieri & Yukiko Hirabayashi & Alessandra Bianchi & Ignazio Mongelli & Katja Frieler & Richard A. Betts & Luc Feyen, 2018. "Increased human and economic losses from river flooding with anthropogenic warming," Nature Climate Change, Nature, vol. 8(9), pages 781-786, September.
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