Unveiling An Efficient Framework for Predicting Flood Risk Areas, Using Earth Observatory Data, Google Earth Engine, and Multicriteria Decision Making-Analytical Hierarchy Process

Authors

  • Ishaku Bashir Yakubu Department of Geography, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria Author https://orcid.org/0000-0001-9471-5047
  • Sheikh D. Abubakar Department of Geography, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria Author https://orcid.org/0000-0002-0497-7797
  • Solomon Ndace Jiya Department of Geography, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria Author
  • Yakubu Muhammad Department of Soil Science, Faculty of Agriculture, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria Author
  • Aisha Yakubu Aliyu Department of Mathematics, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria Author https://orcid.org/0000-0002-9933-8057

DOI:

https://doi.org/10.66050/xfxaaq83

Keywords:

Google Earth Engine, MCDA-AHP, GIS, remote sensing, flood prediction, Niger East

Abstract

The flood risk in the Niger-East region of Niger State is increasingly becoming an annual event. Climatic shifts, land-surface modifications, and human socioeconomic factors are among the conditions that trigger floods. This study explores geospatial technology and multicriteria decision analysis-analytical hierarchy process (MCDA-AHP) to develop a flood risk prediction system that leverages Google Earth Engine to process remote sensing data directly influencing flood risk. Elevation, slope, drainage density, rainfall, soil, proximity to drainage, proximity to road, population density, flow accumulation, and land use land cover (LULC). The weightage assignment was performed using the MCDA-AHP technique. Flood risk classes predicted as very low, 13.82 km2 (9.29%), low, 18.77 km2 (12.61%), low – moderate, 111.97 km2 (75.24%), high, 3.32 km2 (2.23%), and very high, 0.93 km2 (0.63%) of the study area, respectively. This research presents a flood emergency response system that highlights the impact of different prioritization criteria across multiple conditions. Therefore, integrating GEE to generate different flood-conditioning risk indicators, prioritized and ranked using MCDA-AHP, is crucial for developing an efficient methodological framework for flood risk prediction across a wide region, achieving 88% precision. Thus, effective for evidence-based decision-making by authorities, policy makers, and emergency response agencies.

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Author Biographies

  • Sheikh D. Abubakar, Department of Geography, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria

    Department of Geography, Ibrahim Badamasi Babangida University, Lapai 

    Professor

  • Solomon Ndace Jiya, Department of Geography, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria

    Department of Geography, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria.

  • Yakubu Muhammad, Department of Soil Science, Faculty of Agriculture, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria

    Department of Soil Science, Faculty of Agriculture, Ibrahim Badamasi Babangida University, Lapai

  • Aisha Yakubu Aliyu, Department of Mathematics, Faculty of Physical Sciences, Ibrahim Badamasi Babangida University, PMB 11, Lapai, Niger State, Nigeria

    Department of Mathematical Sciences, Ibrahim Badamasi Babangida University, Lapai

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2026-03-28

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Vol. 8 No. 1 (2026): International Journal of Disaster Risk Management

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Copyright (c) 2026 Ishaku Bashir Yakubu, Sheikh D. Abubakar, Solomon Ndace Jiya, Yakubu Muhammad, Aisha Yakubu Aliyu (Author)

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Yakubu, I. B., Abubakar, S. D., Jiya, S. N., Muhammad, Y., & Aliyu, A. Y. (2026). Unveiling An Efficient Framework for Predicting Flood Risk Areas, Using Earth Observatory Data, Google Earth Engine, and Multicriteria Decision Making-Analytical Hierarchy Process. International Journal of Disaster Risk Management, 8(1), 201-228. https://doi.org/10.66050/xfxaaq83

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