Machine Learning-Based Prediction and Analysis of Air and Noise Pollution in Urban Environments

The growth of environmental issues such as climate change, noise pollution, and air and water pollution poses significant challenges due to factors like population growth, industrialization, and fossil fuel usage. Recent techniques in environmental monitoring, including wavelet transformation, neuro-fuzzy systems, and deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown promise. However, these systems face challenges such as data quality, model interpretability, and scalability. Monitoring and mitigating these problems are essential for public health and a sustainable future. This paper focuses on leveraging machine learning algorithms to analyze and predict air and noise pollution levels, with a specific case study of New Delhi, India, a city heavily impacted by these issues. Using datasets collected from trusted sources, including measurements of pollutants like CO2, NO2, SO2, and noise levels, various machine learning algorithms such as Decision Trees and Random Forest are applied and compared for their effectiveness. Performance metrics including accuracy, F-score, precision, and recall are calculated to evaluate the algorithms' performance. The results indicate that Decision Trees and Random Forest outperform other algorithms, demonstrating promise for accurate air and noise pollution analysis. This study underscores the importance of employing machine learning techniques for proactive environmental monitoring and management in urban areas.