Research Article Open Access

Application of Time Series Modeling to Study River Water Quality

Maryam Ghashghaie1, Kaveh Ostad-Ali-Askari2, Saeid Eslamian3 and Vijay P. Singh4
  • 1 Bu-Ali Sina University, Iran
  • 2 Islamic Azad University, Iran
  • 3 Isfahan University of Technology, Iran
  • 4 Texas A and M University, United States
American Journal of Engineering and Applied Sciences
Volume 11 No. 2, 2018, 574-585

DOI: https://doi.org/10.3844/ajeassp.2018.574.585

Submitted On: 19 March 2018 Published On: 25 April 2018

How to Cite: Ghashghaie, M., Ostad-Ali-Askari, K., Eslamian, S. & Singh, V. P. (2018). Application of Time Series Modeling to Study River Water Quality. American Journal of Engineering and Applied Sciences, 11(2), 574-585. https://doi.org/10.3844/ajeassp.2018.574.585

Abstract

Water deficit problem originates from two factors: population increase and water pollution. However, studying and forecasting the quality of water are necessary to avoid serious problems in future through managerial works. In present study, using time series modeling, the quality of Madian Rood River is studied at Baraftab station using time series analysis. Nine parameters of water quality are studied such as: TDS, EC, HCO3-, Cl-, SO42+, Ca2+, Mg2+, Na+ and SAR. Investigation of observed time series shows that there is a common increasing trend for all parameters unless Na+ and SAR. The order of models for each parameter was determined using Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) of time series. The ARIMA model was used to generate and forecast the quality of stream flows. Akaike Information Criterion (AIC), Determination Coefficient (R2), Root Mean Square Error (RMSE) and (Volume Error in Percent (VE %) criteria were referred to evaluate the generation and validation results. The Results show that time series modeling is quite capable of water quality forecasting. For the majority of forecasts, the value of R2 was greater than 0.6 between predicted and observed values.

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Keywords

  • ARIMA
  • Time Series
  • Trend Elimination
  • Water Quality