1- ایزدی، ع.، ک. داوری، ا. علیزاده، ب. قهرمان و س. ا.حقایقی مقدم. 1386. پیش بینی سطح ایستابی با استفاده ازشبکه عصبی مصنوعی (مطالعه موردی: دشت نیشابور). مجله آبیاری و زهکشی ایران. 71 -59 :(2)1.
2- Aytek, A., and O. Kisi. 2008. A geneticprogramming approach to suspended sedimentmodeling. J. Hydrol. 351: 288-298.
3- Babovic, V., and M. Keijzer. 2002. Rainfallrunoff modeling based on geneticprogramming . Nordic Hydrol. 33: 331-343.
4- Banzhaf, W., P. Nordin, R. E. Keller, and F.D. Francone. 1998. Genetic Programming.Morgan Kaufmann, San Francisco, CA.
5- Daliakopoulos, N. I., P. Coulibaly, and I. K.Tsanis. 2005. Ground water level forecastingusing artificial neural networks. J. Hydrol.309: 229-240.
6- Drecourt, J. P. 1999. Application of neuralnetworks and genetic programming to rainfallrunoff modeling. D2K Technical Rep. 0699-1-1. Danish Hydraulic Institute, Denmark.
7- Elizondo, D.A., R. W. McClendon, and G.Hoogenboom. 1994. Neural network modelsfor predicting flowering and physiologicalmaturity of soybean. Trans. ASAE. 37(3):981-988.
8- Ferreira, C. 2001. Gene expressionprogramming: a new adaptive algorithm forsolving problems. Complex Syst. 13(2): 87-129.
9- Francl, L. J., and S. Panigrahi. 1997.Artificial neural network models of wheat leafwetness. Agric. Forest. Meteorol. 88: 57-65.
10- Goldberg, D. E. 1989. Genetic algorithmsin search, optimization, and machine learning.Addison –Wesley, Reading, Mass.
11- Jang, J. S. R. 1993. ANFIS: adaptivenetwork-basedfuzzy inference system. IEEETrans. Syst. Manage. Cyber. 23(3): 665-685.
12- Jang, J. S. R., C. T. Sun, and E. Mizutani.1997. Neurofuzzy and soft computing: a
computational approach to learning andmachine intelligence. Prentice-Hall, NewJersey.
13- Keskin, M. E., O. Terzi, and D. Taylan.2004. Fuzzy logic model approaches to dailypan evaporation estimation in WesternTurkey. Hydrol. Sci. J. 49(6): 1001-1010.
14- Khu, S. T., S. Y. Liong, V. Babovic, H.Madsen, and N. Muttil. 2001. Geneticprogramming and its application in real- timerunoff forming. J. Am. Water Resour. Assoc.37(2): 439-451.
15- Kisi, O., and O. Ozturk. 2007. Adaptiveneurofuzzy computing technique forevapotranspiration estimation. J. Irrig. Drain.Engin. 133(4): 368-379.
16- Liong, S. Y., T. R. Gautam, S. T. Khu, V.Babovic, M. Keijzer, and N. Muttil, 2002.Genetic programming: A new paradigm inrainfall runoff modeling. . J. Am. WaterResour. Assoc. 38(3): 705-718.
17- Lippman, R. 1987. An introduction tocomputing with neural nets. IEEE ASSP Mag.
4: 4-22.
18- Moghaddamnia, A., M. Ghafari Gousheh,J. Piri, S. Amin, and D. Han. 2009. Evaporatinestimation using artificial neural networks andadaptive neurofuzzy inference systemtechniques. Advanc. water Resour. 32: 88-97.
19- Muttil, N., and S. Y. Liong. 2001.Improving runoff forecasting by input variable selection in GP. In: Proceedings of worldwater congres, ASCE.
20- Paruelo, J. M., and F. Tomasel. 1997.Prediction of functional characteristics of
ecosystems: a comparison of artificial neuralnetworks and regression models. Ecolog.Model. 98: 173-18.