AI-POWERED ENVIRONMENTAL SURVEILLANCE: ENHANCING AIR AND WATER QUALITY MONITORING THROUGH REAL-TIME PREDICTIVE ANALYTICS - Gamit, Vrunda; Vasava, Mehul J; Patel, Avani S (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The accelerating pace of urbanization, industrialization, and population growth has intensified global environmental challenges, particularly in air and water quality management. Traditional environmental surveillance systems, while useful, often lack real-time responsiveness, predictive capabilities, and scalability to handle today’s dynamic ecological concerns. Recent technological advances in Artificial Intelligence (AI) and the Internet of Things (IoT) have created transformative opportunities for environmental monitoring by enabling real-time, predictive, and adaptive solutions. AI-powered systems leverage machine learning (ML), deep learning (DL), and predictive modeling to detect pollution levels, forecast environmental patterns, and enhance decision-making for sustainability strategies. This paper reviews the integration of AI and IoT in air and water quality monitoring, focusing on predictive analytics for real-time pollution detection and pattern recognition. It examines AI algorithms for anomaly detection, IoT sensor frameworks for continuous monitoring, and predictive models for urban sustainability strategies. Case studies demonstrate successful implementation in smart cities, industrial emission management, and river basin monitoring. Furthermore, the paper highlights the potential of AI to support proactive interventions, mitigate environmental risks, and strengthen sustainability policies. Challenges remain, including algorithmic bias, cybersecurity risks, high costs of IoT infrastructure, and the need for transparent governance frameworks. Ethical issues, particularly related to privacy and equitable deployment, are critical for responsible adoption. Nonetheless, the integration of AI and IoT into environmental surveillance offers a paradigm shift from reactive systems to predictive, data-driven governance mechanisms. The findings suggest that AI-powered surveillance systems hold the potential to revolutionize global environmental monitoring and provide long-term strategies for pollution mitigation, public health improvement, and ecological resilience. The success of these systems, however, will depend on interdisciplinary collaboration, investment in smart infrastructure, and the establishment of ethical global frameworks.