Developing An Artificial Intelligence Model For Toxic Pollution Forecasting To Support Environmental Control Towards Sustainable Development

Achieving sustainable industrial growth requires balancing economic expansion with environmental protection.While forecasting toxic emissions is vital for crafting effective policies, a major hurdle remains: interpreting complex predictive models to extract actionable insights for decision-makers.To address this, we developed a comprehensive framework integrating automated data preparation techniques, machine learning, SHAP, and dynamic visualizations.Our approach leveraged a diverse dataset combining the Toxics Release Inventory, economic indicators, and demographic information.We evaluated Random Forest and Extreme Gradient Boosting algorithms against a baseline Multiple Linear Regression.Ultimately, the Random Forest model performed best, securing an impressive R-squared score of 0.90.Applying SHAP to this model allowed us to analyze 16 key features.Our analysis revealed that past emission levels were the strongest predictors of future releases, followed closely by specific industrial and geographic factors.Finally, we created an interactive dashboard to monitor emissions, offering facilities a practical tool to ensure policy compliance and actively drive sustainable development.