Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Abstract

Sustainability depends upon some major climate factors which play a major role in ensuring and assuring that sustainability would be maintained if the range of safe values of parameters are maintained. Linear regression and random forest are few among the machine learning models that were employed in order to determine the dependency of each factor on sustainability. The climate data from Delhi from 1971 to 2020 is utilized for the study considering the variables like temperature, precipitation, humidity and atmospheric carbon dioxide concentration which were collected from various authorized sources such as the Indian Meteorological Department and the Central Pollution control board. After studying various factors involved in determining climate sustainability we found out that temperature and atmospheric carbon dioxide concentration have the greatest impact with a percentage of 45% and 30% respectively. Sectors like agriculture, forestry, energy and water management are majorly dependent on these key deciding factors. The R square value was determined to be 0.86 and 0.82 respectively for machine learning models implemented. We found that the random forest model had a better score in comparison to the linear regression model. With this study we thus found out that, how machine learning models can be trained and tested in order to predict the future outcomes for sustainability. This study demonstrates the importance of climate monitoring for maintaining sustainability.