Data mining from process monitoring of typical polluting enterprise.
Artificial neural network
Data mining
Polluting enterprise
Process monitoring
Variable importance measures
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
29 Aug 2023
29 Aug 2023
Historique:
received:
11
07
2023
accepted:
17
08
2023
medline:
31
8
2023
pubmed:
30
8
2023
entrez:
29
8
2023
Statut:
epublish
Résumé
With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R
Identifiants
pubmed: 37644145
doi: 10.1007/s10661-023-11733-5
pii: 10.1007/s10661-023-11733-5
doi:
Substances chimiques
Wastewater
0
Vehicle Emissions
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1109Subventions
Organisme : Ecological Environment Research and Achievement Extension Project of Zhejiang Province
ID : 2022HT0009
Organisme : Ecological Environment Research and Achievement Extension Project of Zhejiang Province
ID : 2022HT0009
Organisme : Ecological Environment Research and Achievement Extension Project of Zhejiang Province
ID : 2022HT0009
Organisme : Ecological Environment Research and Achievement Extension Project of Zhejiang Province
ID : 2022HT0009
Organisme : Ecological Environment Research and Achievement Extension Project of Zhejiang Province
ID : 2022HT0009
Organisme : Science and Technology Plan Project of Taizhou
ID : 22gyb37
Organisme : Science and Technology Plan Project of Taizhou
ID : 22gyb37
Organisme : Science and Technology Plan Project of Taizhou
ID : 22gyb37
Organisme : Science and Technology Plan Project of Taizhou
ID : 22gyb37
Organisme : Science and Technology Plan Project of Taizhou
ID : 22gyb37
Organisme : Science and Technology Program of Zhejiang Province
ID : 2021C03178
Organisme : Science and Technology Program of Zhejiang Province
ID : 2021C03178
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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