Flood-Prone Area Mapping Using the Integration of Hydrological Data and Google Earth Engine (GEE)

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Page Numbers: 6065-6086
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Published: Aug 31, 2026
Digital Object Identifier: 10.58578/mikailalsys.v4i3.11957
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Authors:
Firman Pindria1, Jonni Mardizal2
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Firman Pindria
Universitas Negeri Padang, Indonesia
Jonni Mardizal
Universitas Negeri Padang, Indonesia

Abstract

Flood susceptibility mapping that integrates hydrological and geospatial information remains limited at the sub-watershed scale, particularly in rapidly developing coastal urban areas. This study maps flood susceptibility in the Batang Kandis Sub-watershed, Padang City, by integrating hydrological data through Google Earth Engine (GEE). A quantitative spatial approach was employed using six parameters: rainfall, elevation, slope, soil texture, land cover, and proximity to rivers. Each parameter was classified, scored, and weighted using the Analytic Hierarchy Process (AHP), after which a weighted overlay was applied to generate the Flood Susceptibility Index (FSI). The results indicate that 58.77% of the study area is classified as having very high flood susceptibility, while 21.39% is classified as highly susceptible. These zones are concentrated primarily in downstream areas characterized by low elevations, gentle slopes, extensive built-up surfaces, and close proximity to river networks. Validation against historical inundation data from Ina-Geoportal demonstrated 97.10% spatial correspondence with areas classified as having high or very high susceptibility. These findings confirm that integrating hydrological and geospatial parameters through GEE provides a reliable approach to identifying flood-prone areas at the sub-watershed scale. The resulting susceptibility map provides an evidence-based foundation for prioritizing flood mitigation, strengthening watershed management, and supporting adaptive spatial planning in rapidly developing coastal urban areas.

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How to Cite
Pindria, F., & Mardizal, J. (2026). Flood-Prone Area Mapping Using the Integration of Hydrological Data and Google Earth Engine (GEE). Journal of Multidisciplinary Science: MIKAILALSYS, 4(3), 6065-6086. https://doi.org/10.58578/mikailalsys.v4i3.11957

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