Volume 8, Issue 15 (9-2017)                   jwmr 2017, 8(15): 102-111 | Back to browse issues page

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Groundwater Level Modeling and Forecasting using the Time Series Models (Case Study: the Plains of Hamadan Province) . jwmr. 2017; 8 (15) :102-111
URL: http://jwmr.sanru.ac.ir/article-1-847-en.html
Abstract:   (2429 Views)
Regarding the reliance of the agricultural and industrial sections and the drinking water on the groundwater resources in Hamadan province, the modeling and forecasting groundwater level fluctuations to utilize the resources is a basic necessity. One of the usual method in this way is the utilization of the time series models that give simply and clearly good short-term forecasts if the models are used in the correct way. Therefore, the raw data of piezometers in the plains of Hamadan province are taken and after the preprocessing job and using the Thiessen polygon, the time series of each plain is formed. The Mann-Kendall test showed deterministic trend in all the time series of the plains which consequently it is needed to detrend by excluding the trend term from the time series. Subsequently, the unit root test is carried out for whether the time series are stationary, and then using the Box-Jenkins method, seasonal ARIMA models are applied to the sample data and the bests are selected. Afterwards, the ARIMA models are used in the 12 months forecasting that gives the good out-of-sample forecasts, which in all the plains the lowest Pearson's correlation coefficient and the highest root mean square error are calculated 0.93 and 0.73 m, respectively, for the Hamadan-Bahar plain. Moreover, the best 12-months forecast is obtained in the Kaboudarahang plain with a Pearson's correlation coefficient of 0.99 and a root mean square error of 0.20 m.
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Type of Study: Research | Subject: Special
Received: 2017/09/18 | Revised: 2017/10/10 | Accepted: 2017/09/18 | Published: 2017/09/18

1. Adamowski, J. and H.F. Chan. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407: 28-40. [DOI:10.1016/j.jhydrol.2011.06.013]
2. Ahn, H. 2000. Modeling of groundwater heads based on second-order difference time series models. Journal of Hydrology, 234: 82-94. [DOI:10.1016/S0022-1694(00)00242-0]
3. Amosson, S., L. Almas, B. Golden, B. Guerrero, J. Johnson, R. Taylor and E. Wheeler-Cook. 2009. Economic Impacts of selected water conservation policies in the Ogallala aquifer. Report on Ogallala Aquifer Project, Texas AgriLife Extension Service, Texas, US, 50 pp.
4. Bierkens, M.F.P. and M. Knotters. 1999. Calibration of transfer function-noise models to sparsely or irregularly observed time series. Water Resources Research, 35(6): 1741-1750. [DOI:10.1029/1999WR900083]
5. Box, G.E.P., G.M. Jenkins and G.C. Reinsel. 2008. Time series analysis: forecasting and control. 4th ed., John Wiley & Sons, Hoboken, New Jersey, US, 746 pp. [DOI:10.1002/9781118619193]
6. Chow V.T. and S.J. Kareliotis. 1970. Analysis of stochastic hydrologic systems. Water Resources Research, 6(6): 1569-1582. [DOI:10.1029/WR006i006p01569]
7. Engel, B.A. and K.C.S. Navulur. 2006. The role of geographical information systems in groundwater engineering. In: Delleur, J. W. (ed.) The handbook of groundwater engineering. 717-732 pp., CRC press, Boca Raton, Florida, US.
8. Guan, X., S. Wang, Z. Gao, Y. Lü and C. Wang. 2011. Groundwater depth forecast based on multi-variate time series CAR model. Transactions of the Chinese Society of Agricultural Engineering, 27(7): 64-69.
9. Hamadan Regional Water Authority, 2014. Basic research reports of the Hamadan province water resources. Hamadan Regional Water Authority co., Hamadan, Iran, 197 pp. (In Persian).
10. Hipel K.W., A.I. McLeod and W.C. Lennox. 1977. Advances in Box-Jenkins modeling 1. model construction. Water Resources Research, 13(1): 567-575. [DOI:10.1029/WR013i003p00567]
11. Hipel, K.W. and A.I. McLeod. 1994. Time series modelling of water resources and environmental systems. Vol. 45 In: Developments in water science, Elsevier, New York, US, 1012 pp.
12. Izadi, A., K. Davari, A. Aliazdeh, B. Ghahreman and S.A. Haghayeghi Moghadam. 2007. Water table forecasting using artificail neural networks. Iranian Journal of Irrigation and Drainage, 1(2): 59-71 (In Persian).
13. Izadi, A., K. Davari, A. Alizadeh and B. Ghahreman. 2008. Application of Panel Data Model in Predicting Groundwater Level. Iranian Journal of Irrigation and Drainage, 2(2): 133-144(In Persian).
14. Knotters, M. and P.E.V. van Walsum. 1997. Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component. Journal of Hydrology, 197: 25-46. [DOI:10.1016/S0022-1694(96)03278-7]
15. Llamas, M.R. and A. Garrido. 2007. Lessons from intensive groundwater use in Spain: economic and social benefits and conflicts. In Giordano, M. and K.G. Villholth (eds) The agricultural groundwater revolution: Opportunities and threats to development. 266-298, CABI, Trowbridge, UK.
16. Mackay, J.D., C.R. Jackson and L. Wang. 2014. A lumped conceptual model to simulate groundwater level time-series. Environmental Modelling & Software, 61: 229-245. [DOI:10.1016/j.envsoft.2014.06.003]
17. Mahdavi, M., B. Farokhzadeh, A. Salajeghe, A. Malakian and Souri M. 2012. Simulation of Hamedan-Bahar aquifer and investigation of management scenarios by using PMWIN. Watershed Management Research, 98: 108-116 (In Persian).
18. Malekinezhad, H. and R. Poorshareiati. 2013. Application and Comparison of Integrated Time Series and Artificial Neural Network Model for Prediction of the Variations of Groundwater Level (Case study: Plain Marvast). Journal of Irrigation Science and Engineering, 36(3): 81-92 (In Persian).
19. Mirzavand, M., J. Sadatinejad, H. Ghasemieh, R. Imani, M. Soleymani Motlagh. 2014. Prediction of ground water level in arid environment using a non-deterministic model. Journal of Water Resource and Protection, 6: 669-676. [DOI:10.4236/jwarp.2014.67064]
20. Naderianfar, M., H. Ansari, A. Ziaie and K. davary. 2011. Evaluating the groundwater level fluctuations under different climatic conditions in the basin Neyshabour. Irrigation and Water Engineering, 1(3): 22-37 (In Persian).
21. Naderianfar, M., H. Ansari, H. Dehghan and M. Salari. 2009. Forecasting the groundwater oscillation of the Nishapur plain using the time series models. National Conference on Sustainable Development Patterns in Water Management, 779-794 pp., Mashhad, Iran (In Persian).
22. Nakhaei, M. and A. Saberi Nasr. 2012. Groundwater oscillation forecasting of the Qorveh plain using wavelet-neuarl network and comparing with the MODFLOW numerical model. Advanced Applied Geology, 1(4): 47-58 (In Persian).
23. Poormohammadi, S., H. Malekinezhad and R. Poorshareyati. 2013. Comparison of ANN and time series appropriately in prediction of ground water table (Case Study: Bakhtegan basin). Journal of Water and Soil Conservation, 20(4): 251-262 (In Persian).
24. Qureshi, A.S., P.G. McCornick, A. Sarwar and B.R. Sharma. 2010. Challenges and prospects of sustainable groundwater management in the Indus Basin, Pakistan. Water Resources Management, 24(8): 1551-1569. [DOI:10.1007/s11269-009-9513-3]
25. Samani, N. 2001. Response of karst aquifers to rainfall and evaporation, Maharlu Basin, Iran. Journal of Cave and Karst Studies, 63(1): 33-40.
26. Shah, T. 2005. Groundwater and human development: challenges and opportunities in livelihoods and environment. Water Science and Technology, 51(8): 27-37. [DOI:10.2166/wst.2005.0217]
27. Shirmohammadi, B., M. Vafakhah, V. Moosavi and A. Moghaddamnia. 2013. Application of several data-driven techniques for predicting groundwater level. Water Resources Management, 27: 419-432. [DOI:10.1007/s11269-012-0194-y]
28. Soltani, G. and M. Saboohi. 2008. Economic and social impacts of groundwater overdraft: the case of Iran. Equity and Economic Development EFR 15th ERF annual conference, 1-16, Cario, Egypt.
29. Souri, A. 2012. Econometrcs with the application of Eviews7. Farhangshenasi Publication and Noor-e Elm Publication, Tehran, Iran, 519 pp (In Persian).
30. Sreekanth, P.D., N. Geethanjali, P.D. Sreedevi, S. Ahmed, N.R. Kumar and P.K. Jayanthi. 2009. Forecasting groundwater level using artificial neural networks. Current science, 96(7): 933-939.
31. Taormina, R., K.W. Chau and R. Sethi. 2012. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Engineering Applications of Artificial Intelligence, 25(8): 1670-1676. [DOI:10.1016/j.engappai.2012.02.009]
32. Trichakis, I.C., I.K. Nikolos and G.P. Karatzas. 2009. Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response. Hydrological Processes, 23(20): 2956-2969. [DOI:10.1002/hyp.7410]
33. Yang, L. 2013. Evaluation of the Impact of Government Policy on the Overuse of Groundwater in the Minqin Basin in China. Computational Water, Energy and Environmental Engineering, 2: 59-68.
34. Yoon, H., S.C. Jun, Y. Hyun, G.O. Bae and K.K. Lee. 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology, 396(1): 128-138. [DOI:10.1016/j.jhydrol.2010.11.002]
35. Zare Abianeh, H., M. Bayat Varkeshi and S. Marofi. 2012. Investigating Water Table Depth Fluctuations in the Malayer Plain. Water and Soil Science, 22(2): 173-190 (In Persian).

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