Machine Learning for Sustainable Urban Water Treatment and Pollution Control
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Abstract
Water pollution and inefficient treatment processes threaten global water sustainability, requiring AI-driven solutions for effective management. This paper explores machine learning techniques in urban water treatment, focusing on AI-powered contamination detection, predictive maintenance for treatment plants, and optimization of wastewater recycling. AI models such as support vector machines (SVMs), neural networks, and anomaly detection algorithms are utilized to enhance water purification processes and reduce environmental impact. Case studies demonstrate how AI-driven water management systems contribute to sustainable urban development and improved public health.
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