• Environmental Engineering Research
• /
• v.25 no.3
• /
• pp.267-273
• /
• 2020

This Hydrokinetic energy system is the process of extracting energy from rivers, canals and others sources to generate small scale electrical energy for decentralized usage. This study investigates the application of Soil and Water Assessment Tool (SWAT) in Geographical Information System (GIS) environment to evaluate the theoretical hydrokinetic energy potentials of selected Rivers (Asa, Awun and Oyun) all in Asa watershed, Kwara state, Nigeria. SWAT was interfaced with an open source GIS system to predict the flow and other hydrological parameters of the sub-basins. The model was calibrated and validated using observed stream flow data. Calibrated flow results were used in conjunction with other parameters to compute the theoretical hydrokinetic energy potentials of the Rivers. Results showed a good correlation between the observed flow and the simulated flow, indicated by ash Sutcliffe Efficiency (NSE) and R² of 0.76 and 0.85, respectively for calibration period, and NSE and R² of 0.70 and 0.74, respectively for the validation period. Also, it was observed that highest potential of 154.82 MW was obtained along River Awun while the lowest potential of 41.63 MW was obtained along River Asa. The energy potentials obtained could be harnessed and deployed to the communities around the watershed for their energy needs.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.95-95
• /
• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.