AI-Powered Anomaly Detection in Air Pollution for Smart Environmental Monitoring
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Abstract
Air pollution is a growing concern due to its adverse effects on human health and the environment [1]. Traditional air quality monitoring stations provide accurate data but are expensive to maintain and limited in coverage [2]. This research explores an AI-based anomaly detection framework to enhance air quality assessment and support the development of virtual monitoring stations [3]. The study utilizes four machine learning techniques—Z-score, Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks—to analyse pollution data [4]. The Z-score method detects extreme pollution values by measuring statistical deviations [5], while Isolation Forest identifies outliers by isolating anomalies in the dataset [6]. Autoencoders, a deep learning approach, learn typical pollution patterns and highlight deviations [7], and LSTM networks forecast air quality trends while identifying unexpected pollution spikes [8]. By integrating these techniques, the proposed system improves pollution monitoring, allowing for real-time detection of anomalies and better forecasting of pollution levels [9]. The findings suggest that AI-driven virtual monitoring stations can provide a scalable, cost-effective alternative to traditional sensorbased systems [10]. This approach has the potential to enhance environmental monitoring, support proactive pollution control measures, and contribute to data-driven policymaking for air quality management [11].
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