Multimodal Air Quality Forecast

Shweta Kulkarni

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 6, 539-545

Abstract: Air quality forecasting plays a crucial role in environmental management and public health protection by predicting pollutant concentrations before they reach hazardous levels. This study focuses on developing a predictive model for air quality using historical meteorological and pollutant data. Various machine learning techniques—such as multiple linear regression, random forests, and long short-term memory (LSTM) neural networks—are explored to capture both linear and nonlinear relationships among variables like temperature, humidity, wind speed, and pollutant concentrations (PM2.5, PM10, NO2, SO2, CO, and O3). The model is trained and validated using real-world datasets collected from monitoring stations, and its performance is evaluated using statistical metrics such as RMSE, MAE, and R2. Experimental results demonstrate that deep learning models provide superior forecasting accuracy compared to traditional approaches. The proposed system can serve as a decision-support tool for government agencies, helping to issue timely air quality alerts and develop effective pollution control strategies.

Keywords: Air Quality Forecasting; Pollutant Concentration; Environmental Monitoring; PM2.5 Prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i6:id:1319

DOI: 10.32628/IJSRST25126379

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