Modeling monthly pan evaporations using fuzzy genetic approach

Abstract

► We estimate monthly pan evaporation ( PE ) by using fuzzy genetic (FG) approach. ► FG estimates are compared with neuro-fuzzy (NF) and neural network (NN) techniques. ► Comparison results indicate the superiority of FG models to the NF and NN models. ► Models are also compared in estimating one station’s PE using nearby station’s data. ► Results reveal that monthly PE can be successfully estimated by FG approach. This study investigates the ability of fuzzy genetic (FG) approach in estimation of monthly pan evaporations. Various monthly climatic data, that are, solar radiation, air temperature, relative humidity and wind speed from two stations, Antalya and Mersin, in Mediterranean Region of Turkey, were used as inputs to the FG technique so as to estimate monthly pan evaporations. In the first part of the study, FG models were compared with neuro-fuzzy (ANFIS), artificial neural networks (ANNs) and Stephens–Stewart (SS) methods in estimating pan evaporations of Antalya and Mersin stations, separately. Comparison of the models revealed that the FG models generally performed better than the ANFIS, ANN and SS models. In the second part of the study, models were compared to each other in two different applications. In the first application the input data of Antalya Station were used as inputs to the models to estimate pan evaporation data of Mersin Station. The pan evaporation data of Mersin Station were estimated using the input data of Antalya and Mersin stations in the second application. Comparison results indicated that the FG models performed better than the ANFIS and ANN models. Comparison of the accuracy of the applied models in estimating total pan evaporations showed that the FG model provided the closest estimate. It was concluded that monthly pan evaporations could be successfully estimated by the FG approach.