Journal of Engineering and Applied Sciences
Year:
2012
Volume:
7
Issue:
2
Page No.
134 - 142
References
Abde-Al, M.A. and M.E. El-Hadidi, 1994. A machine learning approach to modeling and forecasting the minimum temperature at Haahran. Saudi Arabia Energy, 19: 739-749.
Arganis, M., R. Val, J. Prats, K. Rodriguez, R. Dominguez and J. Dolz, 2009. Genetic programming and standardization in water temperature modeling. Adv. Civil Eng., Vol. 2009, 10.1155/2009/353960
Aytek, A. and O. Kisi, 2008. A genetic programming approach to suspended sediment modelling. J. Hydrol., 351: 288-298.
CrossRef | Brunt, D., 1941. Physical and Dynamic Meteorology. 2nd Edn., Cambridge University Press, New York.
Dombayci, O.A. and M. Golcu, 2009. Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy, 34: 1158-1161.
CrossRef | Elizondo, D.A., R.W. McClendon and G. Hoogenboom, 1994. Neural network models for predicting flowering and physiological maturity of soybean. Trans. ASAE., 37: 981-988.
Direct Link | Ferreira, C., 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Syst., 13: 87-129.
Direct Link | Ghorbani, M.A., R. Khatibi, A. Aytek, O. Makarynskyy and J. Shiri, 2010. Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput. Geosci., 36: 620-627.
CrossRef | Haykin, S., 1998. Neural Networks: A Comprehensive Foundation. 2nd Edn., Prentice-Hall, Upper Saddle River, New Jersey, Pages: 842.
Jang, J.S.R., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern., 23: 665-685.
CrossRef | Direct Link | Khu, S.T., S.Y. Liong, V. Babovic, H. Madsen and N. Muttil, 2001. Genetic programming and its application in real-time runoff forecasting. J. Am. Water Resour. Assoc., 37: 439-451.
CrossRef | Kisi, O. and O. Ozturk, 2007. Adaptive neurofuzzy computing technique for evapotranspiration estimation. J. Irrig. Drain Eng., 133: 368-379.
Direct Link | Kisi, O., 2006. Daily pan evaporation modelling using a neuro-fuzzy computing technique. J. Hydrol., 329: 636-646.
CrossRef | Direct Link | Liong, S.Y., T.R. Gautam, S.T. Khu, V. Babovic, M. Keijzer and N. Muttil, 2002. Genetic programming: A new paradigm in rainfall runoff modeling. J. Am. Water Resourc. Assoc., 38: 705-718.
CrossRef | Lughofer, E., 2003. Online adaptation of Takagi-Sugeno fuzzy inference systems. Technical Report, Fuzzy Logic Laboratorium, Linz-Hagenberg.
Moghaddamnia, A., M.G. Gousheh, J. Piri, S. Amin and D. Han, 2009. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv. Water Resour., 32: 88-97.
CrossRef | Nash, J.E. and J.V. Sutcliffe, 1970. River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol., 10: 282-290.
CrossRef | Direct Link | Nasseri, M., A. Moeini and M. Tabesh, 2011. Forecasting monthly urban water demand using extended Kalman filter and genetic programming. Expert Syst. Applic., 38: 7387-7395.
CrossRef | Direct Link | Nayak, P.C., K.P. Sudheer, D.M. Rangan and K.S. Ramasastri, 2004. A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol., 291: 52-66.
CrossRef | Nayak, P.C., K.P. Sudheer, D.M. Rangan and K.S. Ramasastri, 2005. Short-term flood forecasting with a neurofuzzy model. Water Resourc. Res., Vol. 41, 10.1029/2004WR003562
Panigrahi, S. and L.J. Francl, 1997. Artificial neural network models of wheat leaf wetness. Agric. Forest Meteorol., 88: 57-65.
Direct Link | Paruelo, J.M. and F. Tomasel, 1997. Prediction of functional characteristics of ecosystems: A comparison of artificial neural networks and regression models. Ecol. Modell., 98: 173-186.
CrossRef | Patterson, D.W., 1996. Artificial Neural Networks: Theory and Applications. Prentice Hall, Singapore, Pages: 477.
Robinson, C. and N. Mort, 1997. A neural network system for the protection of citrus crops from frost damage. Comput. Electron. Agric., 16: 177-187.
CrossRef | Smith, B.A., G. Hoogenboom and R.W. McClendon, 2009. Artificial neural networks for automated year-round temperature prediction. Comput. Electron. Agric., 68: 52-61.
CrossRef | Ustaoglu, B., H.K. Cigizoglu and M. Karaca, 2008. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol. Appl., 15: 431-445.
CrossRef | Whigham, P.A. and P.F. Crapper, 2001. Modelling rainfall-runoff using genetic programming. Math. Comput. Modell., 33: 707-721.
CrossRef |