Optimization of the HEC-RAS Based Flood Inundation Mapping using Adaptive Neuro- Fuzzy Inference System: A case study of Olkeriai River Basin, Kenya

Effective flood modelling is essential in flood disasters’ impact reduction and sustainable land use planning, particularly in vulnerable areas such as the Olkeriai River Basin in Kenya. This study provides an innovative hybrid model of Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Hydrologic Engineering Center-River Analysis System (HEC-RAS) model for improved spatial accuracy in flood inundation mapping. The coupled model provides flood inundation mapping for the whole catchment area unlike HEC-RAS model which is restricted to the defined river bank lines. Flooding in the Olkeriai River basin continues to disrupt riparian agriculture and settlements in this basin, but most conventional hydrological models tend to not accurately simulate flood extents over varied terrain. The steady flow simulation in HEC-RAS was used to simulate a 100-yr return period flood with peak flows from a calibrated hydrologic model in Hydrologic Engineering Center- Hydrologic Modelling System (HEC-HMS). Historical events of flooding and conditioning factors were used to train ANFIS model to create a spatial flood Inundation index map. Lastly, HEC-RAS flood depth inundation outputs were calibrated by overlaying them on the flood inundation index map based on ANFIS model outputs. Results indicate that ANFIS model worked well in terms of accuracy and prediction (R² = 0.960, RMSE = 0.092, MAE = 0.090 and AOC = 0.910), and hybrid model enhanced flood prediction capability (R²= 0.944, RMSE = 0.445, MAE = 0.337 and NSE = 0.944). Derived flood inundation map delineates the high-risk areas within and outside the river corridor. These outcomes will enable local authorities, disaster managers, and planners to implement effective actions in flood mitigation, plan early warnings, and assist land-use planning that renders the community more resilient.