The Combination of Mathematical and Stochastic Models to Improve the Results of Groundwater Models

Document Type : Research Paper

Authors

1 Assistant professor of hydrology, Faculty of Geosciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 MSc of hydrogeology, Faculty of Geosciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Professor of hydrogeology, Faculty of Geosciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

4 Assistant professor of hydrology, Faculty of Geosciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

Groundwater is one of the most important water supplies for domestic, industrial and agricultural use and it is the only reliable source of water in some areas. These resources have been exploited in many countries in the world. The various consequences of improper management and exploitation of groundwater have caused global concern, especially in developing countries. Due to the non-uniform distribution of precipitation and surface water resources and some advantages of groundwater‏ such as low pollution, constant temperature etc., groundwater is at the center of attention as compared to surface water. Therefore, excessive exploitation of these resources will result in irreversible damage, such as declining groundwater level, discharge of wells and quants‏. To reduce the damage caused by the loss of aquifers, optimal management of these water resources is required. In order to achieve this goal, the latest scientific and engineering techniques should be used by water specialists. In this regard, the use of groundwater models and simulation techniques is one way to monitor, control and apply different management scenarios on groundwater resources. In groundwater models, it is possible to determine the response of aquifers in various stresses by defining different management scenarios. Therefore, the models can be used as a useful tool for identifying the hydrogeological system, choosing optimal management options and predicting the impact of climate parameters in the water management systems (Wang and Anderson, 1998; Chitsazan and Tavasuli, 1998). Precipitation, as one of the most important parameters in groundwater numerical models, plays a crucial role in water balance calculation. One of the weak points of mathematical models is the use of precipitation values in the calibration period to predict of the future. To solve the problem, time series models are considered as a solution. Among the specific characteristics of stochastic models is their ability to simulate climatic conditions. The most important time series model is the Autoregressive and Moving Average (ARIMA). The combined use of numerical and stochastic models can be effective to reduce forecast errors of numerical models.

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Main Subjects

References

1-    Abdolahnejad, K., 2015. Forecasting of monthly rain data by stochastic time series models, case study: Hashemabad rain station. Geographical Planning of Space Quarterly Journal, 5(17), pp. 15-25. (In Persian).
 
2-    Anderson, M. and Woessner, W., 1992. Applied Groundwater Modeling Simulation of Flow and Adjective Transport. Academic Press.
 
3-    Azizi, GH. and Roshan, A., 2006. Study of dry-wet periods and possibility of forecasting them by using bolt winters time series model in Hormozgan province. Geographical Researches Quarterly Journal, 20(4), pp. 48-63. (In Persian).
 
4-    Bails, D.G .and Peppers, L.C., 1993. Business fluctuations: forecasting techniques and applications. Englewood Cliffs. Prentice-Hall, Inc.
 
5-    Box, G.E.P and Jenkins, G.M., 1976. Time series analysis: „Forecasting and control. Holden-Day, San Francisco.
 
6-    Chatfield, C., 1996. The analysis of time series: an introduction. Chapman & Hall.
 
7-    Tavasuli, M., 1998. Study and management of water resources of Mahyar North plain using mathematical model, Thesis, Shahid Chamran University of Ahvaz, Iran. 185p. (In Persian).
 
8-    Farhadimanesh, M., 2014. Management of water resources of Sabily-Sarbihis plain (east Dezful) using finite difference mathematical model. MSc. Thesis, Shahid Chamran University of Ahvaz, Iran. 195p. (In Persian).
 
9-    Ghahramani, N. and Gharakhani, A., 2011. Evaluation of time series models for estimating of pan evaporation (case study: Shiraz station). Journal of Water Research in Agriculture, 1(25), pp. 75-81.
 
10- Golabi, M., Akhundaali, M.A., Radmanesh, F. and Kashifipour, M., 2014. Comparison of prediction accuracy of Box-Jinkens models in seasonal precipitation modeling (Case study: selected stations of Khuzestan province). Geographic research journal, 3(29), pp. 72-61. (In Persian).
 
11- Izadi, A., Davari, K., Alizadeh, A. and Ghahraman, B., 2008. Application of panel data model in prediction groundwater level. Iranian journal of irrigation and drainage, 2(2), pp. 133-143. (In Persian).
 
12- Jahanbakhsh, S. and Torabi, S., 2004. Review and prediction of temperature and precipition in Iran. Geographical Researches Quarterly Journal, 19(3), pp. 104-125. (In Persian).
 
13- Karamuz, M. and Araghinejad, Sh., 2005. Advanced hydrology. Amirkabir University of Technology. (In Persian).
 
14- Komornık, J., Komornıkova, M., Mesiar, R., Szokeova, D. and Szolgay, J., 2006. Comparison of forecasting, performance of nonlinear models of hydrological time series. Physics and Chemistry of the Earth Journal, 31, pp. 1127–1145.
 
15- Konikow, L.F. and Kendy, E., 2005. Groundwater depletion, a global problem. Hydrogeology Journal, 13(1), pp. 317–320.
 
16- Mahdavi, Gh., 2011. The study of optimal advantage of Bazman aquifer with MODFLOW code, Thesis, University of Sistan and Baluchestan. Iran. (In Persian).
 
17- Mirzaee, S.Y., 2005. Forecasting of groundwater fluctuations using time series models in Shahrekord plain. Msc. Thesis, Shahid Chamran University of Ahvaz, Iran. 220p. (In Persian).
 
18- Miyanabadi, H. and Afshar, A., 1999. Application of periodic monthly time series in monthly runoff production and integrated water resources management in Kashfarud basin. In 3th National Conference on Water Resources Management, Tabriz university, Tabriz, Iran.
19- Pourhaghi, A., Akhundaali, M.A., Radmannish, F. and Mirzaee, S.Y., 2014. Management of underground water resources in drought conditions with MODFLOW, model case study: Nourabad Plain. Journal of Irrigation Engineering and Science. 2 (37), pp. 71-82. (In Persian).
 
20- Sethi, R.R., Kumar, A., Sharma, S.P. and Verma, H.C., 2010. Prediction of water table depth in a hard rock basin by using artificial neural network. International Journal of Water Resources and Environmental Engineering, 2(4), pp. 95–102.
 
21- Shabani, B., Mousavi, M., Noqabi, M. and Ghahraman, B., 2014. Modeling and predicting maximum and minimum temperature of Mashhad plain using time series models. Water and Soil Journal, 27(5), pp. 896-906. (In Persian).
 
22- Shamsai, A., 1998. Hydraulic flow in porous medium, Application of mathematical models. Amir Kabir University of Technology Publications. (In Persian).
 
23- Shirmohammadi, B., Vafakhah, M., Moosavi, V. and Moghaddamnia, A., 2013. Application of several data- driven techniques for predicting groundwater level. Water Resources Management, 27, pp. 419-432. (In Persian).
 
24- Thomas, H.A., Fiering, M.B., 1962. Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation. In: Design of Water Resources Systems. Harvard University Press, Cambridge, Mass.
 
25- Wang, D., Shannon, M.C., Grieve, C.M. and Yates, S.R., 2000. Soil water and temperature regimes in drip and sprinkler irrigation, implications to soybean emergence. Agricultural Water Management Journal, 43, pp. 15-28.
 
26- Wang, H.F. and Anderson, P., 1998. Introduction to Groundwater Modeling: Finite Difference and Finite Element Methods. Academic Press, San Diego.
 
27- Wei, W.W.S., 1990. Time series analysis. Addison-Wesley Publishing Company, Inc.
 
28- Yang, Q., Wang, Y.; Zhang, J. and Delgado, J., 2017. A comparative study of shallow groundwater level simulation with three-time series models in a coastal aquifer of South China. Applied Water Science Journal, 7(2), pp. 689–698.
 
29- Yidana, S.M., Ganyaglo, S., Yakubo, B.B. and Akabzaa, T., 2011. A conceptual framework of groundwater flow in some crystalline aquifers in southeastern Ghana. Journal of African Earth Sciences, 59(2–3), pp.185-194.
 
30- Zakaria, S., Al-Ansari, N., Knutsson, S. and Al-Badrany, T., 2012. ARIMA models for weekly rainfall in the semi-arid Sinjar district at Iraq. Journal of Earth Sciences and Geotechnical Engineering, 2(3),pp.25-55.
 
31- Zhang, G.P., 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing Journal, 50, pp.159–175.
 
32- Zhou, H.C., Peng, Y. and Liang, G.H., 2008. The research of monthly discharge predictor-corrector model based on wavelet decomposition. Water Resource Management. 22, pp.217–227.
Irrigation Sciences and Engineering
Volume 43, Issue 3
October 2020
Pages 57-70

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History

  • Receive Date: 09 September 2017
  • Revise Date: 15 June 2018
  • Accept Date: 19 June 2018
  • Publish Date: 22 September 2020

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