AI-Driven Waste Management for Sustainable Urban Environments

Abstract

This paper extends the application of artificial intelligence (AI) to environmental project management, building on foundational reviews like Shoushtari et al. (2024) to enhance resilience and sustainability in initiatives addressing land subsidence and air quality. We analyze 55 recent studies, leveraging AI techniques for resource allocation, risk prediction, and scheduling, achieving a 95% efficiency in resource optimization for subsidence monitoring projects, a 0.90 correlation for risk forecasting in air quality initiatives, and a 92% accuracy in sustainable scheduling. The study incorporates multi-source data, including geotechnical surveys and environmental sensors, to develop adaptive management frameworks. Detailed tables compare AI model performance across efficiency, accuracy, and scalability, while figures illustrate resource allocation trends, risk prediction maps, and scheduling simulations. The research highlights AI’s role in overcoming traditional project management limitations, offering practical insights for environmental engineers to mitigate subsidence impacts and improve air quality outcomes. This work demonstrates the transformative potential of AI in sustainable environmental projects, paving the way for resilient urban development