Dataset.

Calibration Software and Data Sets used in: "Multi-sensor data fusion calibration in IoT air pollution platforms" paper

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
The data folder is includes the five different data sets used in the paper along with a metadata file, where the different features are explained., This dataset contains python scripts to calibrate tropospheric ozone sensor data obtained in the H2020 Captor project using sensor fusion techniques. Four different models are implemented; Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN),Random Forest(RF) and Support Vector Regression (SVR). The methodology consists of first applying the PLS procedure to derive orthogonal components (to avoid multicollinearity problems). Afterwards, the components are used as features in the machine learning algorithms, so the models are trained. The scripts available in this repository have been used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal., National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness, Peer reviewed
 

DOI: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106

HANDLE: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
 
Ver en: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
Dataset. 2020

CALIBRATION SOFTWARE AND DATA SETS USED IN: "MULTI-SENSOR DATA FUSION CALIBRATION IN IOT AIR POLLUTION PLATFORMS" PAPER

Digital.CSIC. Repositorio Institucional del CSIC
  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
The data folder is includes the five different data sets used in the paper along with a metadata file, where the different features are explained., This dataset contains python scripts to calibrate tropospheric ozone sensor data obtained in the H2020 Captor project using sensor fusion techniques. Four different models are implemented; Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN),Random Forest(RF) and Support Vector Regression (SVR). The methodology consists of first applying the PLS procedure to derive orthogonal components (to avoid multicollinearity problems). Afterwards, the components are used as features in the machine learning algorithms, so the models are trained. The scripts available in this repository have been used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal., National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness, Peer reviewed

Proyecto: EC/H2020/688110



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