Abstract
Determination of air temperature has a significant role in numerous activities such as agriculture, animal husbandry, industry, highway, airlines and railway transportation. In this study, the monthly average of 67 meteorological parameters, which affects the temperature between 2012 and 2017, has taken from Batman Provincial Directorate of Meteorology and the monthly average air temperature of 2017 has been estimated using the meteorological data from 2012-2016. The estimation process has been carried out using two separate scenarios. In the first scenario, each parameter such as monthly average soil temperature, pressure, water vapour pressure, wind speed and relative humidity have been used in the simple linear regression model as input separately and the monthly average temperature has been estimated. In the second scenario, all 67 parameters have been employed in multi linear regression model as inputs and monthly average temperature has been estimated by this way. As a result, very low root mean square error (RMSE) values has been observed in the range of RMSE= [3.30- 10-55] while very high correlation coefficient (R2) values has been computed in the range of R2= [0.10- 0.99].
Highlights
Air temperature (AT) is one of the main meteorological observations and it plays an important role in an extensive range of implementations such as climate change, terrestrial hydrology and atmospheric sciences [1,2]
All 67 parameters have been utilized as inputs of multi linear regression model and monthly average temperature has been estimated by this way
It can be said that when all features were combined, a very good correlation might be observed for AT estimation
Summary
Air temperature (AT) is one of the main meteorological observations and it plays an important role in an extensive range of implementations such as climate change, terrestrial hydrology and atmospheric sciences [1,2]. It is very effective for the growth and development of plants. The deploying of meteorological stations is generally sparse and unbalanced in sparsely populated zones [5]. Meteorological satellites can provide uninterrupted data for atmospheric and superficial observations over wide coverage terrains [6]
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