Integrating Artificial Neural Networks with Geospatial Analysis to Forecast Future Urban Flood Risk in the Dam-Regulated Shiroro Catchment, Niger State
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Abstract
Urban flood risk in dam-regulated catchments is a dynamic and escalating challenge, driven by the interplay of hydrological modifications, land use change, and climate variability. This study develops and validates a forecasting framework that integrates Artificial Neural Networks (ANN) with geospatial analysis to project future urban flood risk in the Shiroro Dam catchment, Niger State, Nigeria. The framework synergistically combines urban growth simulation, climate change Scenarios, and an ANN model trained on topographic, climatic, land cover, and soil moisture variables. Analysis of multi-temporal Landsat imagery (2014-2024) revealed significant landscape transformation, including substantial agricultural loss and water body expansion due to dam impoundment. The ANN model demonstrated superior predictive performance (accuracy: 91.6%; Kappa: 82%) compared to traditional GIS-overlay methods. It identified 35.06% of the study area as highly vulnerable to flooding, with population densities in risk zones projected to reach 7,318 persons/km² by 2034. The study provides a robust, transferable tool for proactive flood risk management, emphasizing the need for integrated land-use planning and offering a scalable methodology for other dam-affected catchments
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References
Abubakar, I. R., Dano, U. L., & Bala, H. (2018).
Unregulated urban expansion and flooding in
Minna, Nigeria. Environmental Hazards,
(2), 145-163.
Adefisan, E. A. (2018). Climate change projections
over West Africa using RCA4. WASCAL
Technical Report, 1-45.
Ajibola, F. O., Taiwo, O. J., & Aguda, A. S. (2020).
Effects of climate change on extreme rainfall
events over Niger River Basin, Nigeria.
Journal of Water and Climate Change, 11(4),
-1474.
https://doi.org/10.2166/wcc.2019.295
Adepoju, K. A., Lawal, M., & Oladejo, S. (2020).
Land use/land cover dynamics around Shiroro
Dam, Nigeria: Implications for sustainable
management. Remote Sensing Applications:
Society and Environment, 18, 100308.
Adesina, E. A., Musa, A., Ajayi, O. G., Odumosu, J.
O., Opaluwa, Y. D., & Onuigbo, I. C. (2021).
Comparative assessment of SRTM and UAVderived DEM in flood
modelling. Environmental Technology and
Science Journal, 12(2), 58-
https://doi.org/10.4314/etsj.v12i2.6
Adesina, E. A., Odumosu, J. O., Ajayi, O. G., Musa,
A., Onuigbo, I. C., & Adesiji, A. R. (2025).
Evaluating the impact of the Spatial resolution
of digital elevation models on flood modelling.
Water Resource Management, 39, 5359–5390
(2025). https://doi.org/10.1007/s11269-025-
-6
Environmental Technology & Science Journal
Volume 16 Number 2 December 2025
Berhane, G., Tehrany, M. S., & Pham, B. T. (2024).
Advances in machine learning for flood hazard
mapping. Science of The Total Environment,
, 165223.
Dottori, F., Szewczyk, W., Ciscar, J.C., Zhao, F.,
Alfieri, L., Hirabayashi, Y., Bianchi, A.,
Mongelli, I., Frieler, K., Betts, R. A., & Feyen,
L. (2018). Increased human and economic
losses from river flooding with anthropogenic
warming. Nature Climate Change, 8(9), 781-
EM-DAT. (2023). The international disaster
database. Centre for Research on the
Epidemiology of Disasters (CRED),
Universite Catholique de Louvain. Retrieved
from https://www.emdat.be
Gupta, H., Anderson, M., & Kumar, R. (2020).
Urbanization impacts on flood risk in subSaharan Africa. Water, 12(2), 325.
Intergovernmental Panel on Climate Change (IPCC).
(2022). Climate change 2022: Impacts,
adaptation and vulnerability. Cambridge
University Press.
Jongman, B., Ward, P. J., & Aerts, J. C. J. H. (2012).
Global exposure to river and coastal flooding:
Long term trends and changes. Global
Environmental Change, 22(4), 823-835.
https://doi.org/10.1016/j.gloenvcha.2012.07.004
McGrane, S. J. (2016). Impacts of urbanization on
hydrological and water quality dynamics, and
urban water management: A
review. Hydrological Sciences Journal,
(13), 2295-2311.
Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood
prediction using machine learning models:
Literature review. Water, 10(11), 1536.
Nkwunonwo, U. C., Whitworth, M., & Baily, B.
(2020). A review of the current status of flood
risk modelling in Nigeria. Natural Hazards,
, 123-146.
Lu, D., & Weng, Q. (2007). A survey of image
classification methods and techniques for
improving classification performance.
International Journal of Remote Sensing,
(5), 823-870.
https://doi.org/10.1080/01431160600746456
Olanrewaju, O. A., Abubakar, I. R., & Adebayo, O.
S. (2019). Urbanization and flood risk in the
Niger Basin. Environmental Monitoring and
Assessment, 191(4), 242.
Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S.,
Pham, B. T., Mansouri, S., Clague, J. J., Geiss,
C., Ghorbanzadeh, O., & Bahrami, S. (2021).
Flash flood susceptibility mapping using a
novel deep learning model based on deep
belief network, back propagation and genetic
algorithm. Geoscience Frontiers, 12(3),
United Nations Environment Programme (UNEP).
(2021). Dams and development: Relevant
practices for improved decision-making.
United Nations Office for Disaster Risk Reduction
(UNDRR). (2017). Global assessment report
on disaster risk reduction. United Nations.
Vanbelle, S., Engelhart, C. H., & Blix, E. (2024). A
comprehensive guide to study the agreement
and reliability of multi-observer ordinal data.
BMC medical research methodology, 24(1),
Zhang, J., Wang, Z., & Xu, Y. (2016). Accuracy
assessment in remote sensing: Kappa statistics
explained. Remote Sensing Letters, 7(9), 882-