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Development of Adaptive Neuro Fuzzy Inference System for Estimation of Evapotranspiration


Dhruv Patel , The Maharaja Sayajirao University of Baroda, Vadodara; Dr. T. M. V. Suryanarayana, The Maharaja Sayajirao University of Baroda, Vadodara


ANFIS model, Fuzzy Inference, Neural Network, Penman-Monteith, Predicting, Reference evapotranspiration (ETo), Weather Forecast.


The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ETo) is investigated in this paper. The model is based on Adaptive Neurofuzzy Inference System (ANFIS) and uses commonly available weather information such as the daily climatic data, Maximum and Minimum Air Temperature, Relative Humidity, Wind Speed and Sunshine hours from station, Karjan (Latitude - 22°03'10.95"N, Longitude - 73°07'24.65"E), in Vadodara (Gujarat), are used as inputs to the neurofuzzy model to estimate ETo obtained using the FAO-56 Penman.Monteith equation. The daily meteorological data of two years from 2009 and 2010 at Karjan Takuka, Vadodara, are used to train the model, and the data in 2011 is used to predict the ETo in that year and to validate the model. The ETo in training period (Train- ETo) and the predicted results (Test-ETo) are compared with the ETo computed by Penman-Monteith method (PM-ETo) using "gDailyET" Software. The results indicate that the PM-ETo values are closely and linearly correlated with Train- ETo and Test- ETo with Root Mean Squared Error (RMSE) and showed the higher significances of the Train- ETo and Test- ETo. The results indict the feasibility of using the convenient model to resolve the problems of agriculture irrigation with intelligent algorithm, and more accurate weather forecast, appropriate membership function and suitable fuzzy rules.

Other Details

Paper ID: IJSRDV1I4031
Published in: Volume : 1, Issue : 4
Publication Date: 01/07/2013
Page(s): 939-944

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