FLOOD SUSCEPTIBILITY MAPPING IN SOUTHERN PLATEAU STATE, NIGERIA USING FREQUENCY RATIO MODEL AND GEOSPATIAL TECHNIQUES

Authors

  • Songtak National Space Research and Development Agency image/svg+xml Author
  • Jang, P.D. National Space Research and Development Agency image/svg+xml Author
  • Ramnap, N.V. National Space Research and Development Agency image/svg+xml Author
  • Nimlang, H.N. National Space Research and Development Agency image/svg+xml Author
  • Ogbole, A.S. Plateau State University image/svg+xml Author
  • Zitta, Y.L. Telpon Environment Limited Author

DOI:

https://doi.org/10.5281/zenodo.21874498

Keywords:

Geographic Information Systems, Frequency Ratio, Flood Susceptibility Index, Frequency Ratio Model, Model Performance, Flood-Conditioning Factors, Cross Validation

Abstract

Flooding is a significant natural hazard in Sub-Saharan Africa, exacerbated by climate variability, rapid urbanisation, and inadequate drainage systems. This study aims to develop and validate a high-resolution (30 m) flood susceptibility map for Southern Plateau State, Nigeria, using the Frequency Ratio (FR) model and multi-source geospatial datasets. Specifically, the study aims to develop a flood inventory from Sentinel-1 SAR imagery and ancillary records, evaluate the influence of ten flood-conditioning factors, generate a Flood Susceptibility Index (FSI), assess model performance using independent validation and spatial-block cross-validation, and provide a decision-support tool for flood risk management and land-use planning. A flood inventory derived from Sentinel-1 SAR flood extents (2021–2022) and ancillary records was divided into training (70%) and validation (30%) datasets. Ten flood-conditioning factors, including elevation, Topographic Wetness Index, rainfall, land use/land cover, NDVI, lineament density, soil type, geology, and distances to rivers and roads were incorporated into the model. The FR model demonstrated excellent predictive performance (AUC = 0.933; 95% CI: 0.882–0.983), with spatial-block cross-validation confirming its robustness (mean AUC = 0.933 ± 0.034). Rainfall, drainage density, built-up areas, elevation, and lineament density were the most influential flood drivers, while High and Very High susceptibility zones occupied 19.13% of the study area. The resulting susceptibility map provides a reproducible framework and practical decision-support tool for flood risk management, land-use planning, and climate adaptation in data-scarce regions.  State and local planning authorities should integrate the flood susceptibility map into development control, environmental impact assessment, and urban expansion planning to reduce future flood exposure.

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Author Biography

  • Ogbole, A.S., Plateau State University

    Department of Geography

References

Aitsi-Selmi, A., Murray, V., Wannous, C., Dickinson, C., Johnston, D., Kawasaki, A., Stevance, A. S., & Yeung, T. (2016). Reflections on a Science and Technology Agenda for 21st Century Disaster Risk Reduction: Based on the Scientific Content of the 2016 UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. International Journal of Disaster Risk Science, 7(1), 1–29. https://doi.org/10.1007/s13753-016-0081-x

Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834

Breiman, L. (2001). Random Forests (Vol. 45).

Chen, Y., Zhang, X., Yang, K., Zeng, S., & Hong, A. (2023). Modeling rules of regional flash flood susceptibility prediction using different machine learning models. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1117004

Costache, R., Pham, Q. B., Corodescu-Roșca, E., Cîmpianu, C. I., Hong, H., Linh, N. T. T., & Popa, M. C. (2022). Regional flood susceptibility modelling using statistical and machine learning methods. Catena, 210, 105911.

Demissie, Z., Rimal, P., Seyoum, W. M., Dutta, A., & Rimmington, G. (2024). Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment. Applied Computing and Geosciences, 23, 100183. https://doi.org/10.1016/j.acags.2024.100183

Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., & Mosavi, A. (2020). Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of The Total Environment, 705, 135983. https://doi.org/10.1016/j.scitotenv.2019.135983

Dottori, F., Alfieri, L., Rossi, L., Rudari, R., Ward, P. J., & Zhao, F. (2021). Global River Flood Risk Under Climate Change (pp. 251–270). https://doi.org/10.1002/9781119427339.ch14

Kopteer, E.P., Oladosu, O.R., Samson, S.A., Alwadood, J.A., (2024a). A GIS-based assessment of flood impact on agricultural farm activities along river Dilimi, JOS north local government area of Plateau state. World Journal of Advanced Research and Reviews, 21(2), 1018–1024. https://doi.org/10.30574/wjarr.2024.21.2.0456

Kopteer, E.P., Oladosu, O.R., Samson, S.A., Alwadood, J.A., (2024b). A GIS-based assessment of flood impact on agricultural farm activities along river Dilimi, JOS north local government area of Plateau state. World Journal of Advanced Research and Reviews, 21(2), 1018–1024. https://doi.org/10.30574/wjarr.2024.21.2.0456

Fashae, O. A., Tijani, M. N., Talabi, A. O., & Adedeji, O. I. (2014). Delineation of groundwater potential zones in the crystalline basement terrain of SW-Nigeria: an integrated GIS and remote sensing approach. Applied Water Science, 4(1), 19–38. https://doi.org/10.1007/s13201-013-0127-9

Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data, 2. https://doi.org/10.1038/sdata.2015.66

Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X., & Yamazaki, D. (2021). Global exposure to flooding from the new CMIP6 climate model projections. Scientific Reports, 11(1), 3740. https://doi.org/10.1038/s41598-021-83279-w

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd edition). John Wiley & Sons.

Huffman, G. J., tocker, E. F., Bolvin, D. T., Nelkin, E. J., & Tan, J. (2019). GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 [Data set]. NASA Goddard Earth Sciences Data and Information Services Center.

IPCC. (2021). Climate Change 2021: The Physical Science Basis.

Kobayashi, H., & Sanga, T. (2005). Application of spatial information technology to landslides Landslide vulnerability mapping using GIS and spatial statistical analysis. Journal of the Japan Landslide Society, 42(4), 281–292. https://doi.org/10.3313/jls.42.4_281

Montien Tique, W. F., Sapena, M., Weigand, M., Groth, S., Geiß, C., & Taubenböck, H. (2026). A comparative assessment of data-driven flood susceptibility mapping in Nigeria. Natural Hazards, 122(4), 139. https://doi.org/10.1007/s11069-025-07868-y

Nardi, F., Annis, A., Baldassarre, G. Di, Vivoni, E. R., & Grimaldi, S. (2019). GFPLAIN250m, a global high-resolution dataset of earth’s floodplains. Scientific Data, 6. https://doi.org/10.1038/sdata.2018.309

NEMA. (2022). Post-disaster assessment report: 2022 Nigeria floods.

Okoli, E. A., Josephine, K. M., Agoha, C. C., Ikoro, D. O., Oyinebielador, D. O., Aniyom, E. A., Oladipupo, J. T., & Emenyonu, U. D. (2026). Integrated flood susceptibility mapping using machine learning and geospatial techniques: a case study of Imo State, Southeastern Nigeria. Journal of African Earth Sciences, 233, 105872. https://doi.org/10.1016/j.jafrearsci.2025.105872

Ouma, Y., & Tateishi, R. (2014). Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment. Water, 6(6), 1515–1545. https://doi.org/10.3390/w6061515

Pedregosa FABIANPEDREGOSA, F., Michel, V., Grisel OLIVIERGRISEL, O., Blondel, M., Prettenhofer, P., Weiss, R., Vanderplas, J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand, M., Duchesnay, andÉdouard, & Duchesnay EDOUARDDUCHESNAY, Fré. (2011). Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. In Journal of Machine Learning Research (Vol. 12). http://scikit-learn.sourceforge.net.

Pourali, S. H., Arrowsmith, C., Chrisman, N., Matkan, A. A., & Mitchell, D. (2016). Topography Wetness Index Application in Flood-Risk-Based Land Use Planning. Applied Spatial Analysis and Policy, 9(1), 39–54. https://doi.org/10.1007/s12061-014-9130-2

Puno, G. R., Puno, R. C. C., & Maghuyop, I. V. (2022). Flood hazard simulation and mapping using digital elevation models with different resolutions. Global Journal of Environmental Science and Management, 8(3), 339–352. https://doi.org/10.22034/gjesm.2022.03.04

Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Seo, M., & Choi, S.-M. (2023). Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery. Science of The Total Environment, 873, 162285. https://doi.org/10.1016/j.scitotenv.2023.162285

Rentschler, J., Salhab, M., & Jafino, B. A. (2022). Flood exposure and poverty in 188 countries. Nature Communications, 13(1), 3527. https://doi.org/10.1038/s41467-022-30727-4

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. In Ecography (Vol. 40, Number 8, pp. 913–929). Blackwell Publishing Ltd. https://doi.org/10.1111/ecog.02881

Saha, A., Pal, S. C., Arabameri, A., Blaschke, T., Panahi, S., Chowdhuri, I., Chakrabortty, R., Costache, R., & Arora, A. (2021). Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms. Water (Switzerland), 13(2). https://doi.org/10.3390/w13020241

Serdeczny, O., Adams, S., Baarsch, F., Coumou, D., Robinson, A., Hare, W., Schaeffer, M., Perrette, M., & Reinhardt, J. (2017). Climate change impacts in Sub-Saharan Africa: from physical changes to their social repercussions. Regional Environmental Change, 17(6), 1585–1600. https://doi.org/10.1007/s10113-015-0910-2

Shafik, W. (2025). Mapping Flood Hazards in Sub-Saharan African Region (pp. 1–30). https://doi.org/10.4018/979-8-3373-3206-2.ch001

Shamsudduha, M. (2025). Redefining flood hazard and addressing emerging risks in an era of extremes. Npj Natural Hazards, 2(1). https://doi.org/10.1038/s44304-025-00082-7

Singhal, B. B. S., & Gupta, R. P. (2010). Applied hydrogeology of fractured rocks (2nd ed.). Springer Science & Business Media.

Sørensen, R., Zinko, U., & Seibert, J. (2006). On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112. https://doi.org/10.5194/hess-10-101-2006

Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., & Slayback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80–86. https://doi.org/10.1038/s41586-021-03695-w

Timité, N., Kouakou, A. T. M., Bamba, I., Barima, Y. S. S., & Bogaert, J. (2022). Climate Variability in the Sudanian Zone of Côte d’Ivoire: Weather Observations, Perceptions, and Adaptation Strategies of Farmers. Sustainability, 14(16), 10410. https://doi.org/10.3390/su141610410

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0

Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304

UNDRR. (2015). Sendai Framework for Disaster Risk Reduction 2015–2030.

UNDRR. (2020). The human cost of disasters: an overview of the last 20 years (2000-2019) | UNDRR. Https://Www.Undrr.Org/Publication/Human-Cost-Disasters-2000-2019. https://www.undrr.org/publication/human-cost-disasters-overview-last-20-years-2000-2019

Weng, Q. (2001). Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environmental Management, 28(6), 737–748.

Winsemius, H. C., Aerts, J. C. J. H., van Beek, L. P. H., Bierkens, M. F. P., Bouwman, A., Jongman, B., Kwadijk, J. C. J., Ligtvoet, W., Lucas, P. L., van Vuuren, D. P., & Ward, P. J. (2016). Global drivers of future river flood risk. Nature Climate Change, 6(4), 381–385. https://doi.org/10.1038/nclimate2893

Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47–56. https://doi.org/10.1002/esp.3290120107

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Published

2026-08-10

How to Cite

Songtak, N., Jang, P., Ramnap, N., Nimlang, H., Ogbole, A., & Zitta, Y. (2026). FLOOD SUSCEPTIBILITY MAPPING IN SOUTHERN PLATEAU STATE, NIGERIA USING FREQUENCY RATIO MODEL AND GEOSPATIAL TECHNIQUES. International Journal of Renewable Energy and Environment, 4(2), 598-616. https://doi.org/10.5281/zenodo.21874498

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