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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.06777 |
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| _version_ | 1866929750948184064 |
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| author | Barrett, Travis Mishra, Amit Kumar |
| author_facet | Barrett, Travis Mishra, Amit Kumar |
| contents | In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost-effective sensor platforms and possibly extend the time between manual calibration of sensor networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point Barrett, Travis Mishra, Amit Kumar Systems and Control Machine Learning In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost-effective sensor platforms and possibly extend the time between manual calibration of sensor networks. |
| title | Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2503.06777 |