International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 6, Issue:9, September, 2017

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2017.6(9): 3052-3065

Estimation of Pan Evaporation Using Artificial Neural Networks – A Case Study
S. Sajeena*, V. Amrutha Gayathry and V.K. Sharmina
ICAR Krishi Vigyan Kendra Malappuram, Kerala Agricultural University, Tavanur, Malappuram 679573, Kerala, India
*Corresponding author

The process of evaporation is influenced by a number of factors, the most common and important meteorological parameters affecting evaporation are solar radiation, temperature, relative humidity, wind speed and atmospheric pressure. There are number of models for the estimation of evaporation developed by investigators in different fields of study. The interrelated factors are incorporated into various formulae for estimating evaporation. Due to complex interactions between the components of the land-plant-atmospheric systems the reliable estimation of evaporation are extremely difficult. The objective of this study was to develop an evaporation model for the K.C.A.E.T. campus, Tavanur using Artificial Neural Network (ANN) technique and MATLAB software. Monthly observations of meteorological parameters during the period February 2003 to January 2009 collected were used for the model development. The entire data is divided into three categories as pre-monsoon observations, monsoon observations and post monsoon observations. So each category consists of 24 observations of each parameter. From this study, it could be seen that the wind speed had greater influence on the evaporation rate during all seasons. Out of the five strategies considered for the study, the strategy with wind speed, dry bulb temperature and wet bulb temperature (strategy M- 3) was found to be the accurate combination for the estimation of evaporation during all the seasons with comparatively least MSE and high R2 value when compared with other strategies. Hence, it can be concluded that the combination of wind speed, dry bulb temperature and wet bulb temperature has great influence on the evaporation during all seasons.

Keywords: Evaporation, Artificial Neural Network, Wind speed, Dry bulb temperature, Wet bulb temperature and MATLAB.

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How to cite this article:

Sajeena, S., V. Amrutha Gayathry and Sharmina, V.K. 2017. Estimation of Pan Evaporation Using Artificial Neural Networks – A Case Study.Int.J.Curr.Microbiol.App.Sci. 6(9): 3052-3065. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.