Predictive Insight of Global Warming-A Sustainable Control System

Global warming is one of the major issues for decades. Implementation of integrated solution to get sustainable control system has significant challenges. Acceptance of the key precautions and maintenance of sustainability became a bottleneck. The consequence of human social activities increases the consumption of fossil fuels as power resources which indirectly increased the concentration of greenhouse gases and water vapor, in the environment. This causes the earth's average surface temperature to increase. Water vapor is responsible for two-thirds of this issue; however, C02 is proven to be the key factor of global warming. The global temperature could have increased to 3.8 o C in case C02 concentration was doubled. The trend and association between rising temperatures and C02 emissions are shown in this work, along with an explanation of the many methods behind ideal mitigation. One of the goals of this research is to identify the issue area and integrate the climatic data. This will assist in obtaining a dependable control system capable of addressing global warming concerns. This paper outlines how data engineering and analytics may be used to regulate global warming and provides an architecture for the Green Warming Model (GWD). Therefore, the primary contribution of this study effort is environment preservation using data engineering and machine learning (ML).