Evolutionary neural networks for monthly pan evaporation modeling

Estimating pan evaporation is very important for monitoring, survey and management of water resources. This study proposes the application evolutionary neural networks (ENN) for modeling monthly pan evaporations. Solar radiation, air temperature, relative humidity, wind speed and pan evaporation data from two stations, Antalya and Mersin, in Mediterranean Region of Turkey are used in the study. In the first part of the study, ENN models are compared with those of the fuzzy genetic (FG), neuro-fuzzy (ANFIS), artificial neural networks (ANN) and Stephens–Stewart (SS) methods in estimating pan evaporations of Antalya and Mersin stations, separately. Comparison results indicate that the ENN models generally perform better than the FG, ANFIS, ANN and SS models. In the second part of the study, models are compared with each other in estimating Mersin’s pan evaporations using input data of both stations. Results reveal that the ENN models performed better than the FG, ANFIS and ANN models. It was concluded that monthly pan evaporations can be successfully estimated by the ENN method. The performance of the ENN model with full weather data as inputs presents 0.749 and 0.759 mm of mean absolute error for the Antalya and Mersin stations, respectively.