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Abstract
This study advances the field of low-cost air quality sensor calibration by integrating sophisticated machine learning techniques with time series analysis. We combine advanced algorithms such as Support Vector Regression (SVR) and Multilayer Perceptrons (MLP) with SARIMA models to improve calibration accuracy across various sensor types and particulate matter sizes. Our Comprehensive approach reveals the superiority if ensemble methods incorporating SA RIMA components and highlights the varying performance of algorithms across different sensor types and particulate matter sizes. Key findings include the critical importance of temporal features and the persistent challenge in PM10 calibration compared to PM2.5. The results underscore the potential of hybrid approaches that leverage both machine learning and time series analysis, enhancing calibration accuracy and model generalizability across diverse environmental conditions. This work contributes to the development of more reliable and accessible air quality monitoring systems, with significant implications for public health and environmental management.
Original language | English |
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Title of host publication | Proceedings of the 2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON) |
Subtitle of host publication | Lima, Peru, 6-8 November 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Print) | 979-8-3503-7834-4 |
DOIs | |
State | Published - 2024 |
Event | XXXI INTERCON 2024: 2024 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing - INTERCON - Universidad San Ignacio de Loyola , Lima, Peru Duration: 6 Nov 2024 → 8 Nov 2024 Conference number: 979-8-3503-7834-4/24 |
Congress
Congress | XXXI INTERCON 2024 |
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Abbreviated title | XXXI INTERCON 2024 |
Country/Territory | Peru |
City | Lima |
Period | 6/11/24 → 8/11/24 |
Keywords
- Support vector machines
- Analytical models
- Machine learning algorithms
- Accuracy
- Atmospheric modeling
- Computational modeling
- Time series analysis
- Air quality
- Calibration
- Monitoring
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Enhanced Calibration Techniques for Low-Cost Particulate Matter Monitors
Espezúa Llerena, S. (Speaker)
8 Nov 2024Activity: Participating in an event