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Estimation of daily pan evaporation using adaptive neural-based fuzzy inference system

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The main aim of this research was to estimate daily pan evaporation (E
P
) using adaptive neural-based fuzzy inference system (ANFIS) method in the semi-arid region of Iran. The daily climatic data of the five gauging stations which are located in Isfahan Province, including maximum and minimum temperature, maximum and minimum relative humidity, wind speed and sunny hours are introduced as input data and pan evaporation as output data. Based on the obtained structures, three ANFIS models have been tested against the measured pan evaporation to assess the accuracy of each model. The best estimation pan evaporation values is an ANFIS1 model using five input parameters, including maximum and minimum temperature, mean relative humidity, wind speed and sunny hours were obtained with RMSE = 0.72 mm/day, R
2
= 0.95. In ranking the models, ANFIS2 and ANFIS3 ranked in a second and third place, respectively. Thus, in this study, the ANFIS1 model works well for the dataset used.

Keywords: pan evaporation, climatic data, adaptive neural-based fuzzy inference system, ANFIS, neural networks, fuzzy logic, semi-arid regions, Iran, temperature, relative humidity, wind speed, sunshine

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