Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data

TL;DR

This paper compares machine learning and statistical models to analyze climate change impacts, emphasizing pollution, using time series data to improve environmental impact forecasting and mitigation strategies.

Contribution

It introduces a comparative analysis of ML and statistical models specifically for climate change impact assessment with a focus on pollution-related data.

Findings

ML models outperform statistical models in accuracy

Regional differences significantly affect model performance

The study provides insights for better environmental impact prediction

Abstract

Industrial operations have grown exponentially over the last century, driving advancements in energy utilization through vehicles and machinery.This growth has significant environmental implications, necessitating the use of sophisticated technology to monitor and analyze climate data.The surge in industrial activities presents a complex challenge in forecasting its diverse environmental impacts, which vary greatly across different regions.Aim to understand these dynamics more deeply to predict and mitigate the environmental impacts of industrial activities.