Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning

• URL: http://arxiv.org/abs/2108.00640v1
• Date: Mon, 2 Aug 2021 05:19:23 GMT
• Title: Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning
• Authors: Kalpit Yadav, Vipul Arora, Sonu Kumar Jha, Mohit Kumar, Sachchida Nand Tripathi
• Abstract summary: Low-cost particulate matter sensors are transforming air quality monitoring. These sensors require training data from co-deployed reference monitors. We propose novel transfer learning methods for quick calibration of sensors.
• Score: 11.352677351165246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.

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