In the present study, seasonal autoregressive integrated moving average (SARIMA) time series models, nonlinear BL model, multi-layer perceptron artificial neural network and SARIMA-bilinear hybrid models were employed to predict the quality parameters of total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC) in the Maroon basin in Khuzestan Province. For fitting the mentioned models, the monthly data were used for the calibration (1970–2000), confirmation (2001–2011) and prediction of the model (2012–2018). The appropriate models of SARIMA, the bilinear BL and SARIMA-BL hybrid models of the rationalized parameters of the above-mentioned quality parameters were selected based on the adequacy tests, such as the Akaike criterion and the independence test of the model residuals (Ljung–Box). To determine the effective input parameters of the network, the partial mutual information (PMI) algorithm was used to model the three monthly EC, TDS, and SAR parameters. Also, for input and output layers linear transfer function and for hidden layer various active functions with the back-propagation learning algorithm were used for modeling and predicting this water quality parameters. Comparison of the models showed the tangible superiority of SARIMA-bilinear hybrid models than the artificial neural network with effective input parameters based on PMI algorithm, SARIMA and bl models in performed prediction of the all three monthly qualitative parameters in all three stages, training-validation-test in Maroon basin.
In this study, the data at Idenak hydrometric station were used to predict the inflow to Maroun Dam reservoir. For this purpose, different models such as artificial intelligence, Holt-Winters and hybrid models were used. Partial mutual information algorithm was used to determine the input parameters affecting modeling the monthly inflow by artificial intelligence models. According to the Hempel and Akaike information criterion, we introduced the monthly inflow with a 3-month lag, and the temperature with a 1-month lag, with respect to the lowest values of Akaike and the highest values of Hempel as input parameters of artificial intelligence models. The results showed the weak performance of the Holt-Winters model compared to other models and confirmed the superiority of the Holt-adaptive network-based fuzzy inference system (ANFIS) hybrid model with the root-mean-square error of 54 and the coefficient of determination (R2) of 0.83 in the testing process compared to other mentioned models. In addition, the above hybrid models performed better than other models in the test process.
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