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ANFIS-based approach for predicting sediment transport in clean sewer

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adapt...

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Detalles Bibliográficos
Autores principales: Azamathulla, H. Md., Ab. Ghani, Aminuddin, Fei, Seow Yen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273703/
https://www.ncbi.nlm.nih.gov/pubmed/22389640
http://dx.doi.org/10.1016/j.asoc.2011.12.003
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author Azamathulla, H. Md.
Ab. Ghani, Aminuddin
Fei, Seow Yen
author_facet Azamathulla, H. Md.
Ab. Ghani, Aminuddin
Fei, Seow Yen
author_sort Azamathulla, H. Md.
collection PubMed
description The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r(2) = 0.98 and RMSE = 0.002431) compared to the existing predictor.
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spelling pubmed-32737032012-03-01 ANFIS-based approach for predicting sediment transport in clean sewer Azamathulla, H. Md. Ab. Ghani, Aminuddin Fei, Seow Yen Appl Soft Comput Short Communication The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r(2) = 0.98 and RMSE = 0.002431) compared to the existing predictor. Elsevier 2012-03 /pmc/articles/PMC3273703/ /pubmed/22389640 http://dx.doi.org/10.1016/j.asoc.2011.12.003 Text en © 2012 Elsevier B.V. This document may be redistributed and reused, subject to certain conditions (http://www.elsevier.com/wps/find/authorsview.authors/supplementalterms1.0) .
spellingShingle Short Communication
Azamathulla, H. Md.
Ab. Ghani, Aminuddin
Fei, Seow Yen
ANFIS-based approach for predicting sediment transport in clean sewer
title ANFIS-based approach for predicting sediment transport in clean sewer
title_full ANFIS-based approach for predicting sediment transport in clean sewer
title_fullStr ANFIS-based approach for predicting sediment transport in clean sewer
title_full_unstemmed ANFIS-based approach for predicting sediment transport in clean sewer
title_short ANFIS-based approach for predicting sediment transport in clean sewer
title_sort anfis-based approach for predicting sediment transport in clean sewer
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273703/
https://www.ncbi.nlm.nih.gov/pubmed/22389640
http://dx.doi.org/10.1016/j.asoc.2011.12.003
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