Predicting and Analysing Global Warming using Artificial Intelligence

Abstract

Global Warming refers to an increase in average global temperature. Natural Events and human activities are believed to be contributing to increase in average global temperatures. Long Term effects of climate change are frequent wildfires, longer periods of drought in some regions and an increase in the number, duration and intensity of tropical storms. Prediction of Global Warming can be of major importance in agricultural, energy and medical domain. This paper evaluates performance of several algorithms in annual global warming prediction, from previous measured values over the Globe. The first challenge is creating a reliable, efficient and accurate data model on large dataset and capture relationship between the average annual temperatures and potential factors that contributes to global warming such as concentration of Greenhouse gases. The data is predicted and forecasted using linear regression for obtaining the highest accuracy for greenhouse gases and temperature compares to other methods. After observing the analyzed and predicted data, global warming can be reduced comparatively within few years. The reduction of global temperature can help us prevent harmful long-term effects of Global warming and Climate change.