Multifaceted Analysis of Climate Trends and Air Quality in Indian Metropolises: A Machine Learning and Time Series Forecasting Approach

This study presents a comprehensive analysis of climate patterns and air quality in three major Indian cities: Bangalore, Chennai, and Delhi NCR, leveraging over three decades of data. Employing a fusion of machine learning techniques like Gradient Boosting and traditional methods such as ARIMA, we scrutinize the interplay of urbanization, climate shifts, and pollution levels. Through extensive data pre-processing, feature engineering, and model validation, we provide nuanced insights. Our analysis reveals escalating temperatures over the decades with varying precipitation trends and persistent high pollution levels, intensified by seasonal events and industrial emission. The predictive models exhibit high accuracy (83%) in forecasting temperature extremes and air quality indices, offering invaluable inputs for urban planning and public health policymaking. By bridging existing research gaps and adopting a multifaceted approach, this study contributes a comprehensive understanding of the environmental challenges confronting Indian urban centres, thereby facilitating informed decision-making for sustainable development.