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.
Highlights
Climate change becomes more and more important and effects hydrologic circle and water resources day by day
We investigated that if modeling of daily pan evaporation with using much less parameters than Penman-Monteith, is possible and compared the results of employed Artificial Neural Network (ANN) techniques including Levenberg-Marquardt algorithm based Feed Forward Back Propagation neural networks (LMANN), radial basis neural networks (RBNN), generalized regression neural networks (GRNN)
Mean temperature (Tmean) and relative humidity (RHmean), wind speed at 2 m level (Wmean), maximum and minimum temperatures (Tmax, and Tmin) and total solar radiation (Rad) have been used as input-data for Penman-Monteith equation and ANN-models have been set by using these inputs
Summary
Climate change becomes more and more important and effects hydrologic circle and water resources day by day. Evaporation is one of the most important parameters of hydrologic circle and its prediction is getting more important because of climate change effects. Both designing the water works and planning and management of. We investigated that if modeling of daily pan evaporation with using much less parameters than Penman-Monteith, is possible and compared the results of employed ANN techniques including Levenberg-Marquardt algorithm based Feed Forward Back Propagation neural networks (LMANN), radial basis neural networks (RBNN), generalized regression neural networks (GRNN)
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