JWARP  Vol.6 No.4 , March 2014
Comparison of Different Ann Approaches in Daily Pan Evaporation Prediction
ABSTRACT

Nowadays, one of the most important effects on water resources under climate change is increasing of free water surface evaporation which depends on the increasing of temperature. In basins, where there are no observed data, free water surface evaporation is taken into account depending on historical temperature and similar data and their long-term statistics. Predicting of real value of evaporation contains some uncertainties. The modeling of evaporation with a small number of predictors has crucial importance on the regions and basins where measurements are not sufficient and/or not exist. In this presented study, daily evaporation prediction models were prepared by using empirical Penman equation, Levenberg-Marquardt algorithm based on "Feed Forward Back Propagation Artificial Neural Networks (LMANN)", radial basis neural networks (RBNN), generalized regression neural networks (GRNN). When the models were compared, it was noticed that the results of neural network models are statistically more meaningful than the Penman equation.


Cite this paper
Dalkiliç, Y. , Okkan, U. and Baykan, N. (2014) Comparison of Different Ann Approaches in Daily Pan Evaporation Prediction. Journal of Water Resource and Protection, 6, 319-326. doi: 10.4236/jwarp.2014.64034.
References
[1]   Kumar, M., Raghuwanshi, S., Singh, R., Wallender, W.W. and Pruitt, W.O. (2002) Estimating Evapotranspiration Using Artificial Neural Network. Journal of Irrigation and Drainage Engineering, 128, 224-233.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)

[2]   Chauhan, S. and Shrivastava, R.K. (2009) Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks. Water Resources Management, 23, 825-837.

[3]   Kim, S. and Kim, S.H. (2008) Neural Networks and Genetic Algorithm Approach for Nonlinear Evaporation and Evapotranspiration Modeling. Journal of Hydrology, 351, 299-317.
http://dx.doi.org/10.1016/j.jhydrol.2007.12.014

[4]   Piri, J., Amin, S., Moghaddam, A., Keshavarz, A., Han, D. and Remesan, R. (2009) Daily Pan Evaporation Modeling in a Hot and Dry Climate. Journal of Hydrologic Engineering, 803-811.
http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000056

[5]   Trajkovic, S., Todorovic, B. and Stankovic, M. (2003) Forecasting Reference Evapotranspiration by Artificial Neural Networks. Journal of Irrigation and Drainage Engineering, ASCE, 129, 454-457.
http://dx.doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454)

[6]   Whitley, R., Medlyn, B., Zeppel, M., Macinnis-Ng, C. and Eamus, D. (2009) Comparing the Penman-Monteith Equation and a Modified Jarvis-Stewart Model with an Artificial Neural Network to Estimate Stand-Scale Transpiration and Canopy Conductance. Journal of Hydrology, 373, 256-266. http://dx.doi.org/10.1016/j.jhydrol.2009.04.036

[7]   Terzi, O. and Keskin, E. (2010) Comparison of Artificial Neural Networks and Empirical Equations to Estimate Daily Pan Evaporation. Journal of Irrigation and Drainage Engineering, 59, 215-225.

[8]   Moghaddamnia, A., Gousheh, M.G., Piri, J., Amin, S. and Han, D. (2009) Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques. Advances in Water Resources, 32, 88-97.
http://dx.doi.org/10.1016/j.advwatres.2008.10.005

[9]   Chang, F.J., Chang, L.C., Kao, H.S. and Wu, G.R. (2010) Assessing the Effort of Meteorological Variables for Evaporation Estimation by Self-Organizing Map Neural Network. Journal of Hydrology, 384, 118-129.
http://dx.doi.org/10.1016/j.jhydrol.2010.01.016

[10]   Abtew, W. (2001) Evaporation Estimation for Lake Okeechobee in South Florida. Journal of Irrigation and Drainage Engineering, 127, 140-147. http://dx.doi.org/10.1061/(ASCE)0733-9437(2001)127:3(140)

[11]   Penman, H.L. (1948) Natural Evaporation from Open Water, Bare Soil and Grass. Proceedings of the Royal Society London A, 194, 120-145. http://dx.doi.org/10.1098/rspa.1948.0037

[12]   Ham, F. and Kostanic, I. (2001) Principles of Neuro Computing for Science and Engineering. MacGraw-Hill, New York.

[13]   Hagan, M.T. and Menhaj, M.B. (1994) Training Feed forward Techniques with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5, 989-993. http://dx.doi.org/10.1109/72.329697

[14]   Broomhead, D.S. and Lowe, D. (1988) Multivariate Functional Interpolation and Adaptive Network. Complex Systems 2, 321-355.

[15]   Specht, D.F. (1991) A General Regression Neural Network. IEEE Transactions Neural Network, 2, 568-576.
http://dx.doi.org/10.1109/72.97934

[16]   Okkan, U., Serbes, Z. and Dalkilic, H.Y. (2011). Yapay Sinir Aglari ve Ampirik Yontemlerle Balik Tava Buharlasmalarinin Tahmini (In Turkish, Estimation of fish-pan evaporations with the help of ANN and empirical methods). Ankara, DSI Teknik Bulteni (Technical Bulletin of State Water Authority), #111.

[17]   Okkan, U. and Dalkilic, Y. (2012) Radyal Tabanli Yapay Sinir Aglari ile Kemer Baraji Aylik Akimlarinin Modellenmesi (In Turkish, Modeling of Monthly Flows of “Kemer Dam” with the Help of Radial Based Artificial Network). Ankara, IMO Teknik Dergi (Chamber of CE, Technical Bulletin).

 
 
Top