Forecasting the Amount of Water Consumed in Erbil City using Time Series Model (SARIMA)

  • saman Hussein Mahmood Department of Statistics and Informatics / ‎Administration and Economics‎ College/ Salahaddin University-Erbil
  • Imad Ali Omar Department of Civil Engineering / Engineering College / Salahaddin University-Erbil
Keywords: Forecasting, SARIMA, Water Consumed, Time series

Abstract

Forecasting in the time series is one of the essential subjects in statistical science because its indicators helps the government to make a good plan to get an accurate decision in the future. This research was conducted to analyse the time series data for the amount of water consumed in Erbil City for the years proceeding the period between (1/1/2014) until of (30/6/2019). In the analysis, the study found non-stationary in the time series, so the necessary transformations were taken on the series to remove the non-stationary. Thus, the best model was found among the obtained models, SARIMA (0, 1, 2) (1, 1, 1)12 produces the best results based on the minimum statistical criteria (RMSE, MAE, MAPE) used for comparison.

Finally, using the best model to forecast the water monthly average consumed in Erbil City for 12 months (6 months in 2019 and 6 months in 2020) and the results appeared to be consistent with those actual values ​​in the time series.

References

Adhikari, R and Agrawal, R. 2013. An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613.
Aziz, S. Q. 2007. Liminological observations on the water treatment plants in Efraz. 2nd Environmental Conference-Water, Duhok, Kurdistan, Iraq. April.
Bougadis, J., Adamowski, K. & Diduch, R. 2005. Short term municipal water demand forecasting. Hydrological Processes, 19, 137-148.
Caiado, J. 2009. Performance of combined double seasonal univariate time series models for forecasting water demand. Journal of Hydrologic Engineering, 15, 215-222.
Chen, L. & Zhang, T. 2006. Hourly water demand forecast model based on bayesian least squares support vector machine. Tianjin University Science and Technology, 39, 1037-1042.
Ghiassi, M., Zia, D. K. & Saidane, H. 2008. Urban water demand forecasting with a dynamic artificial neural network model. Journal of Water Resources Planning and Management, 134, 138-146.
Hasan, M. T. & Hasan, S. K. 2017. Forecasting the Amount of Water Consumed in Erbil City using Time Series Model (SARIMA). Zanko Journal for Human science, 21, 415-425.
Jain, A. & Ormsbee , L. E. 2002. Short-term water demand forecast modeling techniques—Conventional methods versus AI. Journal (American Water Works Association), 94, 64-72.
Jain, A., Varshney, A. K. & Joshi, U. C. 2001. Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks. Water resources management, 15, 299-321.
Kim, S., Koo, J., Kim, H. & Choi, Y. 2007. Optimization of pumping schedule based on forecasting the hourly water demand in Seoul. Water Science and Technology: Water Supply, 7, 85-93.
Kitagawa, G. 2010. Introduction to time series modeling, CRC press.
Msiza, I. S., Nelwamondo, F. V. & Marwala, T. Artificial neural networks and support vector machines for water demand time series forecasting. Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, 2007. IEEE, 638-643.
Palit, A. K. & Popovic, D. 2005. Computational Intelligence in Time Series Forecasting, Springer, Germany.
Shareef, K. M. & Muhammad, S. G. 2008. Natural and drinking water quality in Erbil, Kurdistan. Current World Environment. 3(2), 227-238.
Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
Zhou, S., Mcmahon, T., Walton, A. & Lewis, J. 2002. Forecasting operational demand for an urban water supply zone. Journal of hydrology, 259, 189-202.
Published
2019-12-05
How to Cite
1.
Mahmood saman, Omar I. Forecasting the Amount of Water Consumed in Erbil City using Time Series Model (SARIMA). JAHS [Internet]. 5Dec.2019 [cited 13Aug.2020];23(6):285 -296. Available from: http://zancojournals.su.edu.krd/index.php/JAHS/article/view/2594
Section
Articles