摘要
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.
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.
