Academic Journal
Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors
العنوان: | Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors |
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المؤلفون: | Ioannis Christakis, Elena Sarri, Odysseas Tsakiridis, Ilias Stavrakas |
المصدر: | Signals, Vol 5, Iss 1, Pp 60-86 (2024) |
بيانات النشر: | MDPI AG |
سنة النشر: | 2024 |
المجموعة: | Directory of Open Access Journals: DOAJ Articles |
مصطلحات موضوعية: | ozone (O 3 ), nitrogen dioxide (NO 2 ), air quality IoT, low-cost sensing systems, optimization low-cost sensors measurements, LASSO regression, Applied mathematics. Quantitative methods, T57-57.97 |
الوصف: | Air quality is a subject of study, particularly in densely populated areas, as it has been shown to affect human health and the local ecosystem. In recent years, with the rapid development of technology, low-cost sensors have emerged, with many people interested in the quality of the air in their area turning to the procurement of such sensors as they are affordable. The reliability of measurements from low-cost sensors remains a question in the research community. In this paper, the determination of the correction factor of low-cost sensor measurements by applying the least absolute shrinkage and selection operator (LASSO) regression method is investigated. The results are promising, as following the application of the correction factor determined through LASSO regression the adjusted measurements exhibit a closer alignment with the reference measurements. This approach ensures that the measurements from low-cost sensors become more reliable and trustworthy. |
نوع الوثيقة: | article in journal/newspaper |
اللغة: | English |
تدمد: | 2624-6120 |
Relation: | https://www.mdpi.com/2624-6120/5/1/4; https://doaj.org/toc/2624-6120; https://doaj.org/article/ac1b548f4bca48388aa59fa8b8374b11 |
DOI: | 10.3390/signals5010004 |
الاتاحة: | https://doi.org/10.3390/signals5010004 https://doaj.org/article/ac1b548f4bca48388aa59fa8b8374b11 |
رقم الانضمام: | edsbas.D0601E6D |
قاعدة البيانات: | BASE |
تدمد: | 26246120 |
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DOI: | 10.3390/signals5010004 |