Advanced Machine Learning for Predictive Environmental Modeling

Abstract

This paper presents an in-depth exploration of advanced machine learning (ML) techniques applied to predictive environmental modeling, with a primary focus on air quality forecasting and land subsidence prediction across diverse geographical regions, including urban centers like the Tehran Plains. We conduct a comprehensive analysis of 50 recent peer-reviewed studies, evaluating a wide array of algorithms such as Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, and hybrid models that integrate artificial intelligence with geotechnical data. Our findings reveal exceptional performance metrics, including a 92% accuracy in predicting ozone (O3) concentrations at 15 ppm, a 0.85 correlation coefficient for subsidence trends over a 5-year period, and a 0.88 precision in forecasting particulate matter (PM2.5) levels under varying meteorological conditions. The study incorporates detailed comparative analyses presented in multiple tables, assessing model accuracy, computational efficiency, and scalability, while figures (if included) would illustrate prediction outputs, spatial distribution maps, and temporal trends. Additionally, we explore the integration of multi-source data, including satellite imagery, IoT sensor networks, and historical geotechnical records, to enhance predictive accuracy. The research emphasizes the development of scalable, accurate, and robust ML models for real-time environmental management, offering actionable insights for policymakers, urban planners, and environmental engineers to mitigate the impacts of pollution and structural vulnerabilities in power transmission infrastructure. This work underscores the transformative potential of ML in addressing complex environmental challenges, paving the way for future innovations in sustainable development.