Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

Hadi Sanikhani, Ozgur Kisi (), Mohammad Nikpour and Yagob Dinpashoh

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2012, vol. 26, issue 15, 4347-4365

Abstract: This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E pan ). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R 2 ), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E pan . The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data. Copyright Springer Science+Business Media Dordrecht 2012

Keywords: Adaptive neuro-fuzzy inference system; Grid partitioning; Subtractive clustering; Evaporation modeling (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11269-012-0148-4

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