Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).
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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | dataset biblioteca |
| Language: | English |
| Published: |
2019-05
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| Subjects: | Ozone, Calibration, http://aims.fao.org/aos/agrovoc/c_5485, http://aims.fao.org/aos/agrovoc/c_36549, http://aims.fao.org/aos/agrovoc/c_28279, Ozono, Calibración, Sensores, |
| Online Access: | http://hdl.handle.net/10261/217107 |
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