A novel stochastic wastewater quality modeling based on fuzzy techniques.
ANFIS-FCM
Fuzzy technique
Outlier detection
Stochastic model
Wastewater quality parameters
Water resources
Journal
Journal of environmental health science & engineering
ISSN: 2052-336X
Titre abrégé: J Environ Health Sci Eng
Pays: England
ID NLM: 101613643
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
30
01
2020
accepted:
03
09
2020
entrez:
14
12
2020
pubmed:
15
12
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM). These new pre-processing methodsare compared to previously employed non-linear approaches for modelling of wastewater influent/effluent 5-day biochemical oxygen demand (BOD
Identifiants
pubmed: 33312627
doi: 10.1007/s40201-020-00530-8
pii: 530
pmc: PMC7721937
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1099-1120Informations de copyright
© Springer Nature Switzerland AG 2020.
Déclaration de conflit d'intérêts
Conflict of interest declarationThe authors declare no conflict of interest.
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