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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2012
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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. |
format | Online Article Text |
id | pubmed-3273703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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