Publikation: From Fully Physical to Virtual Sensing ...

Stammdaten
Titel: From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art
Untertitel:
Kurzfassung: Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.
Schlagworte: Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Erscheinungsdatum: 20.10.2021 (Online)
Erschienen in: Sensors
Sensors
zur Publikation
 ( MDPI Publishing; )
Titel der Serie: -
Bandnummer: 21
Heftnummer: 21
Erstveröffentlichung: Ja
Version: -
Seite: -
Gesamtseitenanzahl: 6971 S.

Versionen
Keine Version vorhanden
Erscheinungsdatum: 20.10.2021
ISBN (e-book): -
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s21216971
Homepage: https://www.mdpi.com/1424-8220/21/21/6971
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
  • Humans in the Digital Age
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