Machine Learning Model for Predicting Net Environmental Effects

Abstract

Environmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and correlation structures derived from environmental literature and expert knowledge. Our model achieved an R-squared value of 0.67, demonstrating moderate predictive power. Feature importance analysis revealed renewable energy usage (coefficient = 0.71) and public awareness (coefficient = 0.44) as significant positive factors influencing environmental outcomes. Model validation included residual analysis and feature importance assessment, with results suggesting reasonable performance within linear regression constraints. Limitations of our study include reliance on synthetic data, assumption of linear relationships between variables, and limited environmental factors. Notwithstanding, our findings provide insights for environmental policymaking, particularly regarding renewable energy adoption and public awareness campaigns. Future work could focus on incorporating real-world data, exploring non-linear modeling approaches, and expanding the feature set to capture more complex environmental interactions. Our research contributes to data-driven environmental assessment by demonstrating the feasibility of combining both physical and social factors in predictive modeling.