AQ-MultiCal: An Interactive No-Code Machine Learning Platform for Low-Cost Air Quality Sensor Calibration and Comparative Model Analysis
= 0.990 with low prediction error. These results demonstrate that AQ-MultiCal enables accurate sensor calibration and systematic comparison of ML models while improving the accessibility of ML-based calibration through an open-source platform designed for domain experts without programming expertise.
Authors
- Hayrettin Gökozan (ORCID: https://orcid.org/0000-0002-8917-4001)
- Eren C. Karsu Asal (ORCID: https://orcid.org/0000-0001-6392-2668)
- Mehmet Taştan (ORCID: https://orcid.org/0000-0003-3712-9433)
Institutions
- Manisa Celal Bayar University (TR)
Publication Details
- Journal
- Sensors
- Published
- 2026-04-14
- DOI
- https://doi.org/10.3390/s26082398
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- European Education and Culture Executive Agency
- Education, Audiovisual and Culture Executive Agency