Publikation: Data Augmentation for a Virtual-Sensor-...

Stammdaten
Titel: Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring
Untertitel:
Kurzfassung: To better control eutrophication, reliable and accurate information on phosphorus and nitrogen loading is desired. However, the high-frequency monitoring of these variables is economically impractical. This necessitates using virtual sensing to predict them by utilizing easily measurable variables as inputs. While the predictive performance of these data-driven, virtual-sensor models depends on the use of adequate training samples (in quality and quantity), the procurement and operational cost of nitrogen and phosphorus sensors make it impractical to acquire sufficient samples. For this reason, the variational autoencoder, which is one of the most prominent methods in generative models, was utilized in the present work for generating synthetic data. The generation capacity of the model was verified using water-quality data from two tributaries of the River Thames in the United Kingdom. Compared to the current state of the art, our novel data augmentation—including proper experimental settings or hyperparameter optimization—improved the root mean squared errors by 23–63%, with the most significant improvements observed when up to three predictors were used. In comparing the predictive algorithms’ performances (in terms of the predictive accuracy and computational cost), k-nearest neighbors and extremely randomized trees were the best-performing algorithms on average.
Schlagworte: Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Erscheinungsdatum: 17.01.2023 (Online)
Erschienen in: Sensors
Sensors
zur Publikation
 ( MDPI Publishing; )
Titel der Serie: -
Bandnummer: 23
Heftnummer: 3
Erstveröffentlichung: Ja
Version: -
Seite: -
Gesamtseitenanzahl: 1061 S.

Versionen
Keine Version vorhanden
Erscheinungsdatum: 17.01.2023
ISBN (e-book): -
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s23031061
Homepage: -
Open Access
  • Online verfügbar (Open Access)

Zuordnung
Organisation Adresse
Fakultät für Technische Wissenschaften
  Institut für Intelligente Systemtechnologien
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Österreich
   hubert.zangl@aau.at
http://www.uni-klu.ac.at/tewi/ict/sst/index.html
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung
Sachgebiete
  • 102018 - Künstliche Neuronale Netze
  • 102019 - Machine Learning
  • 202036 - Sensorik
Forschungscluster
  • Selbstorganisierende Systeme
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Transportation Informatics Group

Kooperationen
Organisation Adresse
University of Johannesburg
Südafrika
ZA  

Beiträge der Publikation
Keine verknüpften Publikationen vorhanden