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     Research Journal of Applied Sciences, Engineering and Technology


Linear Regression Based Lead Seven Day Maximum and Minimum Air Temperature Prediction in Chennai, India

1K. Ramesh, 2R. Anitha and 3P. SelvaGopal
1Regional Centre, Anna University, Tirunelveli
2K. S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India
3Al Musanna College of Technology, Sultanate of Oman
Research Journal of Applied Sciences, Engineering and Technology  2014  11:2306-2310
http://dx.doi.org/10.19026/rjaset.7.530  |  © The Author(s) 2014
Received: July 19, 2013  |  Accepted: July 31, 2013  |  Published: March 20, 2014

Abstract

The surface temperature is the key determinant for vegetation, animals and human livelihood in a particular location of earth. Timely prediction of minimum and maximum temperature will help in planning and governing very hot and very cold climate. In this study numerical weather parameters based lead seven day minimum and maximum temperature prediction models using multiple linear regression is developed at the location Chennai, India. The result of the analysis states that regression based minimum temperature prediction models provide better accuracy than maximum temperature forecast models with the highest R2 and lowest MAE, RMSE in independent test dataset. The analysis also emphasizes that the prediction performance is good at smaller lead days and it decreases gradually to higher lead days for both minimum and maximum temperature.

Keywords:

Linear regression, temperature forecast,


References

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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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