Abstract
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.
Highlights
Modelling and forecasting of river discharge is of vital importance in water resources management and hydrology
The higher the value of R the better the result. This showed that the result was better in the training phase, follow by combined phase and least in the testing phase
Based on the criteria used, the results showed that Model 1 was able to predict the river discharge with more than 70% degree of accuracy
Summary
Modelling and forecasting of river discharge is of vital importance in water resources management and hydrology It helps in planning, operational analysis and efficient management of reservoirs, flood control measures, hydraulic design of structures such as dams, weirs, bridge crossings, sluice gates, barrages, stilling basins and spillways. Auto regressive integrated moving average (ARIMA) (Maity et al, 2010), autoregressive moving average with exogenous inputs (ARMAX) (Wong et al, 2010), nearest neighbour method (NNM) (Emiroglu et al, 2011), support vector machine (SVM) and Monte Carlo simulation (Wang et al, 2009) have been extensively used All these methods have one or other inherent problems in their effective forecasting because they assume linearity and stationarity in their modelling. ANFIS has been used in the following areas: optimal operation of multipurpose reservoir (Mehta and Jain, 2009), hydrological time series prediction (Zounemat-Kermani and Teashnehalb, 2008), municipal water consumption modeling (Yurdusev and Firat, 2009), stream flow forecasting (Swain and Umamahesh, 2004), sediment volume prediction (Cigizoglu and Alp, 2006), ground water flow prediction, reconstruction of missing precipitation events (Dastorani et al, 2010), short term water level prediction (Erinawati and Fenton, 2012), prediction of scour depth at culvert outlets (Azamathulla and Ghani, 2011) and Evapotranspiration prediction (Firat, 2007)
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