Integrating Artificial Neural Networks with geospatial analysis to forecast future urban flood risk in the dam-regulated Shiroro catchment, Niger State

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.