Investigation of the performance and accuracy of multivariate timeseries models in predicting EC and TDS values of the rivers of Urmia Lake Basin

Document Type: Original Article


1 PhD in Water Science and Engineering, Directorate of Piranshahr health center, Urmia University of medical Sciences

2 Assistant Professor, Department of Water Engineering, University of Urmia, Urmia, Iran

3 Associate Professor, Department of Water Engineering, University of Urmia, Urmia, Iran


Considering the complexity of hydrological processes, it seems that multivariate methods may enhance the accuracy of time series models and the results obtained from them by taking more influential factors into account. Indeed, the results of multivariate models can improve the results of description, modeling, and prediction of different parameters by involving other influential factors. In this study, univariate models (ARMA) and auto-correlated multivariate models with the simultaneous autoregressive moving average model (CARMA) were evaluated for modeling Electrical Conductivity and Total Dissolved Solid parameters of the western stations of Urmia Lake Basin. To use the CARMA models, annual flow rate time series, EC, TDS, SAR, and pH values measured across seventeen hydrometric stations between 1992 and 2013 were used. In the studied statistical period, the river flow in the west of Urmia Lake Basin decreased and experienced an incremental increase compared to the EC and TDS values in river flow. By applying influential parameters in CARMA models, the mean error value of the model in training and experimental stages reduces by 32% and 44% for EC values and 34% and 36% for TDS values, respectively.


1.        Ji Z-G. Hydrodynamics and Water Quality : Modeling Rivers, Lakes, and Estuaries.

2.        Zhang C, Zhang W, Huang Y, Gao X. Analysing the correlations of long-term seasonal water quality parameters, suspended solids and total dissolved solids in a shallow reservoir with meteorological factors. Environ Sci Pollut Res. 2017;24(7):6746-6756.

3.        Tutmez B, Hatipoglu Z, Kaymak U. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Comput Geosci. 2006;32(4):421-433.

4.        Karamouz M, Kerachian R, Akhbari M, Hafez B. Design of River Water Quality Monitoring Networks: A Case Study. Environ Model Assess. 2009;14(6):705-714.

5.        Orouji H, Bozorg Haddad O, Fallah-Mehdipour E, Mariño MA. Modeling of Water Quality Parameters Using Data-Driven Models. J Environ Eng. 2013;139(7):947-957.

6.        Soleimani M, Khalili K, Behmanesh J. Prediction of EC and TDS quality parameters by using changes in River discharge. Case Study: Rivers of Mahabadchay and Balkhlouchay (Bayazid e) located in urmia lake basin (1992-2013). 2017.

7.        Khadr M. Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models. In: The handbook of Environmental Chemistry.Springer, Berlin, Heidelberg; 2017:1-23.

8.        Barzegar R, Adamowski J, Moghaddam AA. Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch Environ Res Risk Assess. 2016;30(7):1797-1819.

9.        Thomas H, M.B.Fiering. Mathematical synthesis of streamflow sequances for the nalysis of river basins by simulation. In: Design of Water Resources Systems. ; 1962.

10.      Salmani MH, Salmani Jajaei E. Forecasting models for flow and total dissolved solids in Karoun river-Iran. J Hydrol. 2016;535:148-159.

11.      Zou P, Yang J, Fu J, Liu G, Li D. Artificial neural network and time series models for predicting soil salt and water content. Agric Water Manag. 2010;97(12):2009-2019..

12.      Shea J. Instrument Relevance in Multivariate Linear Models: A Simple Measure. Rev Econ Stat. 1997;79(2):348-352.

13.      Salas J, Delleur J, Yevjevich V. Applied modeling of hydrologic time series. Littleton, Colorado: Water Resources Publications; 1988.

14.      Matalas NC. Mathematical assessment of synthetic hydrology. Water Resour Res. 1967;3(4):937-945.


16.      Hipel KW. Stochastic and statistical methods in hydrology and environmental engineering. Stoch Hydrol Hydraul. 1995;9(1):1-11.