Estimation of future Reference Crop Evapotranspiration Using Soft Computing Techniques

Authors

  • L Gowri

  • K Sasireka

Received date: July 28, 2018

Accepted date: July 28, 2018

Published date: July 20, 2018

DOI:

https://doi.org/10.14419/ijet.v7i3.12.16438

Keywords:

Evapotranspiration, Artificial Neural Network (ANN), FAO-modified Penman method, Thronthwaite method, Hargreaves method.

Abstract

Estimation of Evapotranspiration forms the basis for computation of irrigation requirement of crop, and also it is considered as one of the vital component of hydrological cycle. This study describes the conceptual outline and implementation to test the ability of an artificial neural network (ANN) for accurate estimation of reference evapotranspiration (ETo). There are many conventional methods like FAO modified Penman method, temperature-based and radiation-based empirical methods are used to estimate (ETo). Among the conventional methods, Thronthwaite method and Hargreaves method perform well in the selected region.  An ANN network is trained to recognize patterns of the daily meteorological variables and their corresponding evapotranspiration which is estimated using FAO-modified Penman method. The advantage of using ANN technique is the network’s ability to use minimum number of meteorological parameters, hence economical.

 

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How to Cite

Gowri, L., & Sasireka, K. (2018). Estimation of future Reference Crop Evapotranspiration Using Soft Computing Techniques. International Journal of Engineering and Technology, 7(3.12), 606-611. https://doi.org/10.14419/ijet.v7i3.12.16438

Received date: July 28, 2018

Accepted date: July 28, 2018

Published date: July 20, 2018