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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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Index Copernicus ICV 2017: 100.00
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Int.J.Curr.Microbiol.App.Sci.2019.8(7): 2122-2127
DOI: https://doi.org/10.20546/ijcmas.2019.807.255


Impact of Metrological Parameters on Reference Evapotranspiration using Multiple Linear Regressions
Yadvendra Pal Singh*, H.K. Mittal, P.K. Singh, S.R. Bhakar and H.K. Jain
Department of Soil & Water Engineering at Maharana Pratap University of Agriculture & Technology, Udaipur (Rajasthan), India
*Corresponding author
Abstract:

The reference evapotranspiration (ET0) of Morena station was estimated using multiple linear regression (MLR), The climatological data such as maximum temperatures, minimum temperature, mean relative humidity, wind speed and solar radiation were collected for the morena station and district of Madhya Pradesh state of India for the period of thirty years and the missing value of that data series was also determine using SPSS-21 software. The observed reference evapotranspiration (ET0) values were estimated using the Penman monteith (FAO56-PM) equation. Multiple Linier Regression is carried out using ET0 as predictor variable and maximum temperatures, minimum temperature, relative humidity, solar radiation and wind speed as independent variable to find out predominant factor on ET0. This whole procedure is done for three different variable based Models. In model-1, Maximum temperature and minimum temperature speed are correlated with ET0. In model-2, Maximum temperature, minimum temperature and solar radiation are correlated with ET0. In model-3, Maximum temperature, minimum temperature, mean relative humidity and wind speed are correlated with ET0. In case of model 3 the value of R, R2 and RMSE for 70% dataset is 0.975, 0.949 and 0.466 respectively and for 30% dataset it is 0.981, 0.962 and 0.607 respectively. As the value of R and R2 are nearer to 3 and the value of RMSE is low, which is good. As the model-3 gives the best correlation values as compared to model-2 and model-1, it can be accepted as the best fit model for prediction of ET0. Considering maximum temperature the model gives good correlation values hence maximum temperature is accepted as predominant factor and the presence of relative humidity does not play an important role in prediction of ET0 for this study area.


Keywords: Multiple linear regression, Performance evaluation, Climate change and reference evapotranspiration
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How to cite this article:

Yadvendra Pal Singh, H.K. Mittal, P.K. Singh, S.R. Bhakar and Jain, H.K. 2019. Impact of Metrological Parameters on Reference Evapotranspiration using Multiple Linear Regressions.Int.J.Curr.Microbiol.App.Sci. 8(7): 2122-2127. doi: https://doi.org/10.20546/ijcmas.2019.807.255