ai-driven prediction of atmospheric particulate matter for air quality assessment
ASWINI M,MOHANAPRIYA S,TEJA M
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 18 April,2025 Pages:1976-1982
Abstract
Air pollution poses a significant threat to public health and environmental sustainability, necessitating accurate forecasting for effective mitigation. Traditional air quality monitoring systems in India rely on stationary sensors and simplistic models, which often fail to provide precise predictions due to their inability to capture complex temporal dependencies in pollutant levels. To overcome these limitations, this study proposes an advanced air pollutant detection system leveraging the Bidirectional Long Short-Term Memory (BI-LSTM) algorithm. The proposed system integrates real-time and historical air quality data obtained from government monitoring stations, satellites, and public databases. Data preprocessing techniques, including missing value handling, normalization, and feature selection, ensure high data integrity. Additionally, temporal feature extraction methods, such as seasonal decomposition and time-lagged analysis, improve model performance by identifying longterm trends and seasonal variations in pollutant levels. The BI-LSTM model is trained using optimized hyperparameters to enhance prediction accuracy and minimize errors. A comparative analysis is conducted against conventional prediction models, including Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and standard Long Short-Term Memory (LSTM) networks. The BI-LSTM model is trained using optimized hyperparameters to enhance prediction accuracy and minimize errors. A comparative analysis is conducted against conventional prediction models, including Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and standard Long Short-Term Memory (LSTM) networks
Kewords
Time series forecasting, air quality monitoring, machine learning, BI-LSTM, deep learning, air pollution detection
Reference
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