Abstract:
The main cause of the ozone layer's depletion, which is a serious
environmental problem, is human activity such as the emission of
chemicals that deplete the ozone layer, like Chloro-Fluro Carbons. The
combination of machine learning (ML) and game theory methods appears
to be a novel and promising way to better anticipate and address ozone
layer depletion. The interactions between different stakeholders, such as
nations or industries, that affect the dynamics of the ozone layer can be
modeled using a framework provided by game theory. In the meantime,
large-scale dataset analysis made possible by Time Series Forecasting
along with correlation allows for more precise forecasts and well-informed
decision-making. This study's main goal is to improve the accuracy of
ozone layer depletion predictions by utilizing ARIMA Time Series
forecasting, correlation with the Air Quality Index along with the science
of strategy for better decision-making via Game Theory. The proposed
methodology has proposed a way to create a more realistic and
comprehensive model by taking into account the strategic interactions
among various entities that contribute to the depletion of the ozone layer.
By using an interdisciplinary approach, we hope to aid in the creation of
practical plans for environmental sustainability and ozone layer protection.
ARIMA predicted the values for the upcoming years, with a Root Mean
Squared Value of 5.04. The Game Theory approach generates a report
tailored to the needs of the user suggesting the protocols to be followed.
Finally, the authors also correlated the Air Quality Index with the Ozone
Layer Depletion with an accuracy of 82% with Gradient Boosting.
