AI-Enhanced Water Resource Management for Climate-Resilient Urban Systems

Abstract

This paper investigates the role of AI-driven waste management in promoting sustainable urban environments, focusing on waste classification, recycling optimization, and landfill monitoring amidst challenges like land subsidence and air pollution in areas such as the Tehran Plains. We analyze 75 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and convolutional neural networks (CNNs), achieving a 96% accuracy in waste classification, a 0.90 correlation for recycling efficiency forecasts, and a 94% precision in detecting landfill subsidence. The study leverages multi-source data, including IoT sensor networks, satellite imagery, and air quality monitors, to develop innovative waste management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures illustrate waste generation trends, recycling rates, and subsidence impacts on landfills. The research underscores AI’s potential to enhance waste processing, reduce environmental pollution, and support urban sustainability, providing actionable insights for policymakers and waste management professionals. This work highlights the transformative impact of AI in building resilient and eco-friendly urban waste systems.