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    Estimation of outflow discharge from an ungauged river: case study of Awara in Ondo State Southwestern, Nigeria
    (2017-10) Alabi, O. O.; Sedara, S. O.; Adetoyinbo, A. A.; Akinwande, D. D.
    Flooding has become the annual experience of Nigerian cities especially in the southwestern states of country, precisely Ondo state. This study uses Muskingum Cunge method river routing techniques to compute outflow discharge and to determine hydrographs of inflow and outflow pattern of Awara river Ikare Akoko, North east LGA in Ondo State, Southwestern of Nigeria. The Muskingum Cunge method of flood routine reflects the interrelationship existing between the inflow and outflow of river. The average monthly inflow discharge for twenty years was worked on using Muskingum Cunge method of flood routine. Observation from the hydrograph shows th'at inflow peak occurs in the month of April, while the outflow in the same month is very low. This implies that the month of April is a critical month that needs a special attention and there is a need for channelization of this river before the month of April every year, in order to prevent occurrence of flooding whenever there is heavy rainfall.
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    Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020
    (John Willey & Sons Limited, 2018) Fashae, O. A.; Olusola, A. O.; Ndubuisi, I.; Udomboso, C. G.
    Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.