Master Thesis / Research Internship: Calibration of low-cost air quality sensors using machine learning techniques

02.05.2022, Diplomarbeiten, Bachelor- und Masterarbeiten

We are interested in studying universal calibration models of low-cost air quality sensors using machine learning techniques and investigating their generalizability with data collected from distant sites.

Low-cost air quality (AQ) sensor networks are potential providers of dense AQ measurements. However, they suffer from decaying performance over time, which requires more frequent calibration. Several machine-learning-based calibration approaches are proposed in the literature with promising results to address this issue.
Nevertheless, the question is, “how reliably these calibrated sensors can perform at a different location?”. To answer this question, we are interested in studying universal calibration models using machine learning techniques and investigating their generalizability with data collected from different and distant sites.

Start: Immediately
Supervisor: M.Sc. Ayah Abu Hani (ayah.abu-hani@tum.de)

Kontakt: ayah.abu-hani@tum.de

More Information