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.

has been cited by the following article:

• TITLE: Comparison of Different Ann Approaches in Daily Pan Evaporation Prediction

  AUTHORS: Yildirim Dalkiliç, Umut Okkan, Nesrin Baykan

  KEYWORDS: Daily Evaporation; Pan Evaporation; Evaporation Prediction; Penman; LMANN; RBNN; GRNN

  JOURNAL NAME: Journal of Water Resource and Protection, Vol.6 No.4, March 27, 2014

  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.