• Environmental Engineering Research
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• v.20 no.3
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• pp.290-297
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• 2015

This paper presents the evaluation of the impact of climate change on water resources and yield capacity of Asa and Kampe reservoirs. Trend analysis of mean temperature, runoff, rainfall and evapotranspiration was carried out using Mann Kendall and Sen's slope, while runoff was modeled as a function of temperature, rainfall and evapotranspiration using Artificial Neural Networks (ANN). Rainfall and runoff exhibited positive trends at the two dam sites and their upstream while forecasted ten-year runoff displayed increasing positive trend which indicates high reservoir inflow. The reservoir yield capacity estimated with the ANN forecasted runoff was higher by about 38% and 17% compared to that obtained with historical runoff at Asa and Kampe respectively. This is an indication that there is tendency for water resources of the reservoir to increase and thus more water will be available for water supply and irrigation to ensure food security.

• Environmental Engineering Research
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• v.25 no.3
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• pp.267-273
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• 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
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• 2023.05a
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• pp.95-95
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• 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.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.3
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• pp.233-244
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• 2015

Marine ecosystem modelling has become a more widely used decision-making tool in coastal ecosystem-based management. However, it is not trivial to develop a well calibrated/validated model with potential applicability and practicality because understanding ecological processes with complexities is a pre-requisite for developing robust ecosystem models and this accompanies a great deal of well coordinated efforts among field-going ecologists, laboratory scientists, modelers, stake-holders and managers. This report aims to provide a brief introduction on two different approaches in marine ecological models: deterministic (mechanistic) and stochastic (statistical) approach. We also included definitions, historical overview of past researches, case studies, and contextual suggestions for coastal management in Korea. A long list of references this report included in this study might be used as an introductory material for those who wish to enter ecosystem modelling field.