Global Warming Prediction in India using Machine Learning
D. Deva Hema1, Anirban Pal2, Vineet Loyer3, Rajeev Gaurav4
1D. Deva Hema*, Assistant Professor in SRM Institute of Science and Technology in Ramapuram, Chennai, (Tamil Nadu), India.
2Anirban Pal, Department of Computer Science and Engineering, from SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3Vineet Loyer, Department of Computer Science and Engineering, from SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
4Rajeev Gaurav, Department of Computer Science and Engineering, from SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
Manuscript received on September 11, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4061-4065 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1301109119/2019©BEIESP | DOI: 10.35940/ijeat.A1301.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Long term global warming prediction can be of major importance in various sectors like climate related studies, agricultural, energy, medical and many more. This paper evaluates the performance of several Machine Learning algorithm (Linear Regression, Multi-Regression tree, Support Vector Regression (SVR), lasso) in problem of annual global warming prediction, from previous measured values over India. The first challenge dwells on creating a reliable, efficient statistical reliable data model on large data set and accurately capture relationship between average annual temperature and potential factors such as concentration of carbon dioxide, methane, nitrous oxide. The data is predicted and forecasted by linear regression because it is obtaining the highest accuracy for greenhouse gases and temperature among all the technologies which can be used. It was also found that CO2 is the plays the role of major contributor temperature change, followed by CH4, then by N20. After seeing the analysed and predicted data of the greenhouse gases and temperature, the global warming can be reduced comparatively within few years. The reduction of global temperature can help the whole world because not only human but also different animals are suffering from the global temperature.
Keywords: Global Warming, Temperature prediction, Greenhouse gases prediction, Linear Regression.