Egbiki S.1,*, Ehiorobo J. O.2 and Izinyon O. C.2
1 Department of Civil Engineering, Faculty of Engineering, Nigerian Maritime University, Okerenkoko, Delta State, Nigeria
2 Department of Civil Engineering, Faculty of Engineering, University of Benin, Benin City, Edo State, Nigeria
*Corresponding Author: email@example.com
Vol. 4 No. 2 | October 2020 | Pages 432 – 449
In this study, the discharge of Ikpoba River was modelled and forecasted using adaptive neuro-fuzzy inference system (ANFIS). The river daily discharge, temperature and precipitation data sets from year 1991 to 1995 were used. In applying the ANFIS, five models stages; model-1, model-2, model-3, model-4 and model-5 were created using MATLAB. Model-1 to 4 were created using only the river discharge data, while model-5 was created by incorporating temperature and precipitation to cater for the effect of climate change into model-4. Five performance evaluation criteria, coefficient of correlation (R), coefficient of determination (R2), mean square error (MSE), modelling efficiency (E) and index of agreement (IOA) were used for comparative analysis. The results showed that though Model 1 to 4 were able to predict the river discharge accurately, model-5 (when the effect of climate change was incorporated) performed better than the other four models with only discharge data. The training phase in model-5 showed an over-estimation of 0.043% of the observed target output sets while an over-estimation of 0.044% was observed in the testing phase. These are within acceptable error tolerance of +/-10% for data validation. This information is useful for integrated water resources planning and management.
Keywords: Adaptive Neuro Fuzzy System, Forecasting, Performance Evaluation, Discharge
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Cite this article as:
Egbiki S., Ehiorobo J. O. and Izinyon O. C., 2020. Modelling and Forecasting of Ikpoba River Discharge in the Niger Delta Region using Adaptive Neuro-Fuzzy Inference System (ANFIS). Nigerian Journal of Environmental Sciences and Technology, 4(2), pp. 432-449. https://doi.org/10.36263/nijest.2020.02.0235