Assessing the Impact of Climate-Change & Developing A Predictive Model for Heatstroke Incidents in Pune, India

Abstract

The increase in occurrences of heatwaves in Pune, India, creates the urgency of developing predictive models to safeguard public health. This research endeavours to construct a robust predictive model to estimate the impact of heatwaves on Pune's population, leveraging machine learning algorithms and historical weather data spanning five years (2016-2020). By analysing meteorological attributes like temperature, humidity, dew point, and wind speed, we aim to discern patterns indicative of heatwave occurrences, enabling proactive mitigation strategies. Through an exhaustive literature review, we explored the potential of machine learning in forecasting heat-related illnesses and identified suitable algorithms for our predictive model. Random Forest Regression, CatBoost Regressor, and XGBoost Algorithm emerged as promising candidates due to their effectiveness in similar contexts. The dataset used comprises comprehensive weather attributes, supplemented by synthetic data representing heat stroke cases under Indian weather conditions. Our study's findings hold promise for policymakers and healthcare authorities, offering actionable insights to mitigate the adverse effects of heatwaves on public health in Pune and beyond.