مدل‌سازی پاسخ سیستم آب‌زیرزمینی به تغییرات مصرف و رواناب سطحی

Abdolahzadeh M, Fakherifard A, Asadi A and Nazemi A.H, 2017. Modeling the Effects of Consumption and Precipitation on the WaterTable Oscillations (Case Study: Ajabshir Aquifer). Water and Soil Science- University of Tabriz, 26(1): 97-83. (In Persian)

Affandi A and Watanabe K, 2007. Daily groundwater level fluctuation forecasting using soft computing technique. Nature and Science 5(2): 1-10.

Coulibaly P, Anctil F, Aravena R and Bobee B, 2001. Artificial neural network modeling of water table depth fluctuations. Water Resources Research 37: 885–896.

Ebrahimi H, Rajaee T, 2016. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change 148: 181-191.

Elizabeth MS, Keith JB and Nick A, 2010. Hydrology in Practice. 4^th ed, 546 p. Amazon.Co.Uk.

Ferreira C, 2001. Gene Expression Programming: a new adaptive algorithm for solving problems. Complex Systems 13(2): 87–129.

Ferreira C, 2006. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer, Berlin: Heidelberg New York, 478.

Frederick GE and Wild KJ, 2003. Nonlinear Regression. Wiley-IEEE. 768p.

Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O and Shiri J, 2010. Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Computers & Geosciences 36: 620-627.

Ghorbani MA, Makarynskyy O, Shiri J and Makarynska D, 2010. Genetic Programming for sea level prediction in an Island Environment. Journal of Ocean and Climate Systems 1: 27-35.

Ioannis N, Daliakopoulos A, Coulibaly P, Ioannis K and Tsanis B, 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology 309: 229–240.

Karamouz M, Kerachian R and Zahraie B, 2004. Monthly water resources and irrigation planning: case study of conjunctive use of surface and groundwater resources. Journal of Irrigation and Drainage Engineering 130(5): 391- 402.

Kavehkar SH, Ghorbani M.A, Ashrafzadeh A and Darbandi S, 2014. Simulation of water level fluctuations using gene expression planning. Civil Engineering and Environment, 43(3): 78-72. (In Persian)

Khasheiy Siyuki A, Ghahreman B and Koochakzade M, 2014. Comparison of Artificial Neural Network Models, ANFIS and Regression in Estimating the Staging Level of the Aquifer in Neishabour Plain. Irrigation and drainage of Iran, 7: 10-22. (In Persian)

Kurtulus B and Razack M, 2010. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy. Journal of Hydrology 381: 101–111.

Makarynskyy O, Makarynska D, Kuhn M and Featherstone WE, 2004. Predicting sea level variations with artificial neural networks at Hillary Harbour, Western Australia. Estuaries, Coastal and Shelf Science 61:351-360.

Mohtasham M, Dehghani A, Akbarpour A, Meftah M and Etebari B, 2011. Groundwater Level Determination by Using Artificial Neural Network (Case study: Birjand Aquiefer). Irrigation and drainage of Iran, 4(1): 9-1. (In Persian)

Nayak P, Satyaji R and Sudheer KP, 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management 2(1): 77-99.

Panda DK, Mishra A, Jena SK, James BK and Kumar A, 2007. The influence of drought and anthropogenic effects on groundwater levels in Orissa, India. Hydrology 343: 140– 153.

Sahoo S, Jha MK, 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrology 21: 1865– 1887.

Suryanarayana C, Sudheer C, Mahammood V, Panigrahi BK, 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145: 324-335.