Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices
| Autor: | Nuria Castell, M. Salvato, G. Di Francia, Kostas Karatzas, E. Esposito, Grazia Fattoruso, S. De Vito |
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| Přispěvatelé: | Francia, G. D., Fattoruso, G., Vito, S. D., Salvato, M., Esposito, E. |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Multivariate statistics
010504 meteorology & atmospheric sciences Artificial neural network Computer science business.industry Locality Distributed air quality monitors 010501 environmental sciences 01 natural sciences Reliability engineering Distributed air quality monitor Calibration methods Mobile chemical multisensory devices Machine learning Knowledge base Robustness (computer science) Mobile chemical multisensory device Calibration method Performance indicator Relocation business Air quality index 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Electrical Engineering ISBN: 9783319668017 |
| DOI: | 10.1007/978-3-319-66802-4_38&partnerID=40&md5=a0a085e202468e50f7688099ff1a5f5f |
| Popis: | The adoption of on field calibration for pervasive air quality monitors, is increasing significantly in the last few years. The sensors data, recorded on the field, together with co-located reference analyzers data, allow to build a knowledge base that is more representative of the real world conditions and thus more effective. However, on field calibration precision may fade in time due to change in operative conditions, due to different drivers. Among these, relocation is deemed among the most relevant. In this work, for the first time, we attempt to assess the robustness of this approach to relocation of the sensor nodes. We try to evaluate the impact on performance of the so called locality issue by measuring the changes in the performance indicators, when a chemical multisensory system operates in a location that differs from the one in which it was on field calibrated. To this purposes, a nonlinear multivariate approach with Neural Networks (NN) and a suitable dataset, provided by NILU (the Norwegian Institute for Air Quality), have been used. The preliminary results show a greater influence of seasonal forcers distribution with respect to the relocation issues. © Springer International Publishing AG 2018. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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