Abstract
Air quality has a significant impact on human health and the environment, making its monitoring and classification extremely important. This review explores the application of machine learning techniques in analyzing and classifying air quality. Various methods such as decision trees, support vector machines, neural networks, and ensemble learning are evaluated to assess their effectiveness in processing complex and multidimensional air sensor data. This study also discusses challenges in data collection and preprocessing, selection of relevant features, and interpretation of classification results. Furthermore, this review identifies recent trends and future research opportunities in the use of machine learning to improve the accuracy and efficiency of air quality monitoring systems. The analysis results show that machine learning techniques have great potential to enhance our understanding of air quality dynamics and support better decision-making in environmental management
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