Water resources assessment activities in inadequately gauged basins are often significantly constrained due to the insufficiency or total lack of hydro-meteorological data, resulting in huge uncertainties and ineffectual performance of water management schemes. In this study, a new methodology of rainfall-runoff modelling using the powerful clustering capability of the self-organising map (SOM), unsupervised artificial neural networks, is proposed as a viable approach for harnessing the multivariate correlation between the typically long record rainfall and short record runoff in such basins. The methodology was applied to the inadequately gauged Osun basin in southwest Nigeria for the sole purpose of extending the available runoff records and, through that, reducing water resources planning uncertainty associated with the use of short runoff data records. The extended runoff records were then analysed to determine possible abstractions from the main river source at different exceedance probabilities. This study demonstrates the successful use of emerging tools to overcome practical problems in sparsely gauged basins.
Modelling and Forecasting of Growth Rate of New COVID-19 Cases in Top Nine Affected Countries: Considering Conditional Variance and Asymmetric Effect
