Abghari, H., 2008. Intelligent prediction methods based on wavelet and neural network models between monthly river discharges. Ph.D. Dissertation, Faculty of Natural Resources, University of Tehran.173pp. (In Persian). 2. Adib, A., Zamani, R., 2015. Spatial variability analysis of groundwater quality indicators Dezful plain using geostatistical method. Journal of Water Resources Engineering 8:1-12. (In Persian). 3. Afifi, E., Yamani, M., Hasanzadeh, Y., 2012. Hydrogeomorphology basin Garu plain (Hormozgan Province). Journal of Geographical land 9(35): 61-76. (In Persian). 4. Askari Marnani, S., Chitsazan, M., Mirzayi, Y., 2001. Investigation of Water Quality in Firoozabad Sub-Chachment in View of Domestic and Agricultural Usage using GIS. P 1-8, the 8 th International Congress on River Engineering, Shahid Chamran University, Iran. (In Persian). 5. Chowdhury, M., Alouani, A., Hossain, F., 2010. Comparison of ordinary kriging and artificial neural network for spatial mapping of arsenic contamination of groundwater. Stochastic Environ. Res. and Risk Assess 24(1): 1-7. 6. El-Shafie, A., Jaafer, O., Seyed, A., 2011. Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia, Int. J. Phys. Sci. 6(12): 2875-2888. 7. Hosseini, S.M., Borhani, R., 2009. The application of artificial neural network in estimating the river yield by minimum temperature and discharge (case study: Hamoon basin). The First International Conference of Water Crisis, 10-12 March. Zabol University. (In Persian). 8. Huiqun, M., Ling, L., 2008. Water quality assessment using artificial neural network .In International Conference on Computer Science and Software Engineering 5-13 December, USA. 9. Imrie, C.E., Durucan, S., Korre, A., 2000. River flow prediction using artificial neural networks :generalisation beyond the calibration range. Journal of Hydrology 233: 138–153. 10. Isazadeh, M., Arabzadeh, R., Darbandi, S., 2016. Performance Evaluation of Geostatistical Methods and Artificial Neural Network in Estimation of Aquifer Quality Parameters(Case Study: Qorveh Dehghan Plain). J. Water and Soil Sci. 20(77): 197-210. (In Persian). 11. Jalali, M., Karami, S., Marj, A.F., 2016. Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer, Iran. Environmental Modeling & Assessment 21(6):707-719. 12. Khodai, K., Shahsavari, A.A., Etebari, B., 2006. Vulnerability assessment Jovin aquifer with DRASTIC and GODS methods. Iranian Journal of Geology 2(4): 73-87. (In Persian). 13. Kisi, O., Shiri, J., Tombul, M., 2012. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences 51: 108-117. 14. Mirzavand, M., Ghasemiyeh, H., Sadatinejad, S.J., Akbari, M., 2015. Simulation of changes in groundwater quality using artificial neural network (case study: Kashan aquifer). Journal of Iranian Natural Resource 68 (1): 159-171. (In Persian). 15. Moosavi, V., Vafakhah, M., Shirmohammadi, B., Behnia, N., 2013. A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water resources management 27(5):1301-1321. 16. Nourani, V., Alami, M.T., Vousoughi, F.D., 2016. Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters. Journal of Hydro informatics 18(2):288-309. 17. Riad, S., Mania, J., Bouchaou, L., Najjar, Y., 2004. Predicting catchment flow in a semi-arid region via an artificial neural network technique. Hydrological Processes Journal, 18(13): 2387–2393. 18. Shirmohammadi, B., Vafakhah, M., Moosavi, V., Moghaddamnia, A., 2013. Application of several data-driven techniques for predicting groundwater level. Water resources management 27(2):419-432. 19. Taheri Tizro, A., Voudouris, K., Vahedi, S., Spatial variation of groundwater quality parameters: a case study from a semiarid region of Iran. International Bulletin of Water Resources & Development 1(3): 1-14. 20. Vafakhah, M., 2012. Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Canadian Journal of Civil Engineering 39(4), 402-414. 21. Vafakhah, M., Mohseni saravi, M., Mahadavi, M., Alavipanah, S.K., 2011. Snowmelt Runoff Prediction by Using Artificial Neural Network in taleghan watershed. Journal of Iran-Watershed Management Science & Engineering 5(14): 23-36. (In Persian). 22. Wang, W.C., Chau, K.W., Cheng, C.h.T., Qiu, L., 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374(34), 323-331. 23. Yu, X., Liongs, S.Y., 2006. Forecasting of hydrologic time series with ridge regression in feature space. Journal of Hydrology 332 (3-4), 290-302. 24. Zare Abyaneh, H., Bayat, M., Akhavan, S., Mohamadi, M., 2011. Estimation of nitrate in groundwater Hamedan-Bahar plain using artificial neural network and data separation effect on prediction accuracy. Journal of Ecology 37(58): 129-140. (In Persian). 25. Zehtabian, Gh., Janfaza, E., Mohammad asgari, H., Nematollahi, M.J., 2010. Modeling of ground water spatial distribution for some chemical properties (Case study in Garmsar watershed). Iranian journal of Range and Desert Reseach 17(1): 61-73. (In Persian).