Comparative evaluation of classical and SARIMA-BL time series hybrid models in predicting monthly qualitative parameters of Maroon river

Abstract

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.

Long-Term Prediction of Climate Variables of the Lake Van Basin: A Comprehensive Analysis Using Statistical and Deep Learning Models

“…∅(𝐵 𝑚 ) and Θ(𝐵 𝑚 ) are polynomials of order P and Q in 𝐵 𝑚 , respectively. ∇ 𝑑 is nonseasonal operator and ∇ 𝑚 𝐷 is seasonal operator (Ahmadpour, Mirhashemi, & Panahi, 2023).…”