Browsing by Author "Ganiyu, Habeeb Oladimeji"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Enhancing Flood Simulation in Data-Sparse Niger Central Hydrological Area River Basin in Nigeria using Machine Learning-Based Data Fusion.
    (Springer Nature, 2026-03-12) Ganiyu, Habeeb Oladimeji; Ng, Cia Yik; Othman, Faridah; Wan Jaafar, Wan Zurina
    Flooding remains a persistent and devastating natural hazard in Nigeria. Accurate precipitation data are critical for hydrological simulation of floods. However, many river basins in Nigeria lack adequate ground-based meteorological data. This has created a need for alternative rainfall data sources, with satellite-based precipitation products (SPPs) offering promising solutions. However, SPP estimates often exhibit biases and uncertainties, particularly in complex tropical environments. Therefore, enhancing SPP accuracy through data fusion is essential for reliable hydrological modelling. This study enhances flood event simulation in a data-sparse Niger Central Hydrological Area River Basin in Nigeria by employing Extreme Gradient Boosting (XGB), Random Forest (RF), and Long Short-Term Memory (LSTM) models to fuse daily downscaled PERSIANN-CDR with observed rainfall data from 2013 to 2022. The RF-fused PERSIANN-CDR exhibited higher accuracy than the XGB and LSTM models. It improved the downscaled PERSIANN-CDR mean correlation coefficient (R) by 113.48% and reduced the mean MAE, RMSE, and Bias by 29.90%, 26.76%, and 75.71%, respectively. The reliability of the RF-fused PERSIANN-CDR was validated hydrologically using the HEC-HMS model. The simulated flow relative to the observed flow indicated improved NSE and R² values, alongside reduced RSR values during the calibration and validation periods, compared to the individual datasets. The robustness of the RF-fused data improvement was further validated using bootstrapped 95% confidence intervals and paired Wilcoxon tests (p<0.05). This study presents a practical framework that demonstrates the potential of data fusion to enhance satellite precipitation estimates for flood simulations, offering a viable approach for improving flood preparedness in data-sparse regions.
  • Thumbnail Image
    Item
    GIS-based approach for morphometric characteristics and development of hydrographs for the upper watershed of Jebba Reservoir, Nigeria
    (Ethiop. J. Sci. Technol., 2021-10) Adeogun , Adeniyi Ganiyu; Mohammed, Apaalando; Ganiyu, Habeeb Oladimeji; Salami, Adebayo Wahab
    Nigeria's Jebba sub-basins are synonymous to frequent flooding, high rate of erosion, depletion of soil nutrients and unsustainable water use. The uncontrolled flooding may be a result of numerous factors related to topography, geology, climate and human activity. The present work was an attempt to describe the application of Geographical Information System (GIS) and Digital Elevation Model (DEM) for the estimation of morphometric characteristics of eight sub-basins in the upstream watershed of Jebba reservoir, Nigeria. Morphometric characteristics such as topographic, areal, relief and network were determined. Soil Conservation Service (SCS) technique was applied to estimate hydrographs. The study revealed that sub-basin number 3 had the lowest time of concentration and maximum depth of runoff while sub-basin number 2 had maximum ratio of circulation of 1.8 and it is tagged as the area that is highly prone to flood. The peak runoff in the sub-basins ranged between 330.10 and 924.86 m3/s (25-year return period) and for 100-year intervals ranged between 502.69 to 1408.40 m3/s. The estimated peak runoffs can be adopted for designing and constructing erosion control structures in the catchment area.