Mohammad Soleimani; kivan khalili; javad Behmanesh
Abstract
Introduction : More than three decades, hydrologists , using multivariate models to describe complex data modeling. While recently the importance of multivariate models have been proposed in hydrology.Indeed, the results of multivariate models can improve the results of description, modeling, and prediction ...
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Introduction : More than three decades, hydrologists , using multivariate models to describe complex data modeling. While recently the importance of multivariate models have been proposed in hydrology.Indeed, the results of multivariate models can improve the results of description, modeling, and prediction of different parameters by involving other influential factors. Methods: In this study, univariate models (ARMA) and auto-correlated multivariate models with simultaneous autoregressive moving average model (CARMA) were evaluated for modeling EC and TDS parameters of the Southern stations of Urmia Lake Basin. In order to employ CARMA models, annual flow rate timeseries, EC, TDS, SAR, and pH values measured across 3 hydrometric stations (Kotar- Balqchy- Gerdyaghob ) within 1992-2013 were used. Findings: The results of the qualitative parameters of the West River basin of Lake Urmia Showed that in the period under review the flow of the studied rivers in the south of Lake Urmia decrease And the EC and TDS values have experienced an increasing trend. EC and TDS values modeling results showed that the average error (RMSE) EC in modeling values equal to 16/60 mho / cm into the teaching and 13/26 mho / cm in the testing phase and for the TDS parameter values 19/84 and 12/71 in the testing phase is the phase of training. The estimated values of the calculation error and accuracy of the model is located entirely within the confidence interval. Conclusion: The results of multivariate modeling EC and TDS values showed that the involvement of the parameters listed in the model , modeling accuracy will be satisfactory.