Estimation of Quantity and Quality Parameters of Groundwater Using Numerical Models (Case Study: Mighan Desert Basin, Arak)

Document Type : Research Paper


1 Department of Civil, Arak Branch, Islamic Azad University

2 Department of Civil Engineering, Islamic Azad University Arak Branch

3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University

4 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Islamic Azad University of Arak Branch, Arak


In this paper, salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, were simulated using four novel artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (OSELM) as well as the MODFLOW software (MT3D model). In order to develop the hybrid artificial intelligence models, the wavelet transform was employed. First, the effective lags in estimating the quality and quantity parameters of the groundwater were identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models were developed using the effective lags for each of the artificial intelligence methods. Then, the superior models in simulating the groundwater quality and quantity parameters were detected by conducting a sensitivity analysis. Subsequently, the most effective lags in estimating these parameters were introduced. In addition, the results of The MODFLOW model were compared with the artificial intelligence models, and it was concluded that the latter were more accurate. For instance, the scatter index and Nash-Sutcliffe efficiency coefficient values for TDS simulation by the superior model were 5.34E-03 and 0.991, respectively. Additionally, RMSE and MAE for estimating groundwater level using the superior model were obtained 0.078 and 0.061, respectively. Finally, uncertainty analysis for the superior models was carried out.


Main Subjects

Azari, T. and Samani, N. (2018). Modeling the Neuman’s well function by an artificial neural network for the determination of unconfined aquifer parameters. Computational Geosciences, 22(4), 1135-1148.
Chelsea, Q. and Wan, Y. (2013). Time series modeling and prediction of salinity in the Caloosahatchee River Estuary. Water Resources Research, 49(9), 5804-5816.
Dong, Y., Li, G. and Xu, H. (2012). An aerial recharge and discharge simulating method for MODFLOW. Computers & geosciences, 42, 203-205
Harbaugh, A.W., Banta, E.R., Hill, M.C. and McDonald, M.G. (2000). MODFLOW-2000, The U. S. Geological Survey Modular Ground-Water Model-User Guide to Modularization Concepts and the Ground-Water Flow Process. Open-file Report. U. S. Geological Survey, (92), 134.
Hendrickx, J.M.H. and Walker, G.R. (1997). Recharge from precipitation. In: Simmers, I., Balkema, A.A. (Eds.), Recharge of Phreatic Aquifers in (Semi-) Arid Areas. Rotterdam, The Netherlands, 19–111.
Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing 70, 489–501.
Kheradpisheh, Z., Talebi, A., Rafati, L., Ghaneian, M.T. and Ehrampoush, M.H. (2015). Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran. Desert, 20(1), 65-71.
Lerner, D. N., Issar, A. S., Simmers, I. (1990). Groundwater recharge: a guide to understanding and estimating natural recharge. Hannover: Heise, (8), 99-228.
Liang, N.Y., Huang, G.B., Saratchandran, P. and Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Networks, 22 (17), 1411–1423.
McDonald M.G. and Harbaugh A.W. (1988). A modular three-dimensional finite-difference ground-water flow model. Techniques of Water-Resources Investigations, 06-A1, USGS.
Nofal, E.R., Amer, M.A., El-Didy, S.M. and Fekry, A.M. (2015). Delineation and modeling of seawater intrusion into the Nile Delta Aquifer: a new perspective. Water Science, 29(2), 156-166.
Priyanka, B.N. and Mahesha, A. (2015). Parametric studies on saltwater intrusion into coastal aquifers for anticipate sea level rise. Aquatic Procedia, 4, 103-108.
Roshni, T., Jha, M.K., Deo, R.C., and Vandana, A. (2019). Development and evaluation of hybrid artificial neural network architectures for modeling spatio-temporal groundwater fluctuations in a complex aquifer system. Water Resources Management, 1-17.
Salami Shahid, E. and Ehteshami, M. (2016). Application of artificial neural networks to estimating DO and salinity in San Joaquin River basin. Desalination and Water Treatment, 57(11), 4888-4897.
Vaheddoost, B. and Aksoy, H. (2018). Interaction of groundwater with Lake Urmia in Iran. Hydrological Processes, 32(21), 3283-3295.
Yang, X., Zhang, H. and Zhou, H. (2014). A hybrid methodology for salinity time series forecasting based on wavelet transform and NARX neural networks. Arabian Journal for Science and Engineering, 39(10), 6895-6905.