Volume : III, Issue : VII, July - 2014

Estimating Reference Crop Evapotranspiration using Neural Network Fitting

Dr. Falguni Parekh

Abstract :

Evapotranspiration is a key parameter foragrometeorological studies and water resources management. This complex process is dependent on climatic factors. There are different methods to predict Reference crop evapotranspiration. In recent years, Artificial Neural Networks (ANN)have been applied as a powerful instrumentto increase predictability capacity of linear and nonlinear relationships in complex engineeringproblems. Use of this tool box in different fields of civil engineering, agriculture, environment, andin particular hydrologic matters for a range of significant parameters with complex mathematicalequations and variables have been addressed.In this studyneural network fitting tool is used for estimating Reference crop evapotranspiration. Meteorological data of Gandhinagarstationis collectedto estimate reference crop evapotranspiration (ETo). Maximum and minimum temperature is used to develop the model. The estimated ETo by Neural Network is compared with ETo determined by Pan Evaporation Method. Coefficient of determination, Mean Square Error and Coefficient of correlations are used to evaluate the model. The study reveals that using ANN, ETo can be accurately predicted with limited climatological data for all months.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Dr. Falguni Parekh Estimating Reference Crop Evapotranspiration using Neural Network Fitting International Journal of Scientific Research, Vol : 3, Issue : 7 July 2014

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