Probabilistic Machine Learning for Environmental Risk Prediction and Risk-Informed Decision-Making Under Uncertainty

This study explores integrating probabilistic machine learning models with risk-informed decision-making frameworks to enhance environmental risk prediction and management in sustainability projects. By employing advanced techniques such as Bayesian Neural Networks, Gaussian Processes, and Monte Carlo simulations, the approach provides predictive insights while quantifying uncertainties associated with environmental hazards, such as climate impacts, pollution, and natural disasters. The uncertainty-aware predictions enable more robust risk assessments, crucial for informed decision-making in sustainability initiatives. This framework helps stakeholders balance environmental, social, and economic trade-offs while optimizing resource allocation and risk mitigation strategies. The methodology is beneficial for managing complex systems where future conditions are uncertain, ensuring resilience and long-term sustainability.