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:
Short Communication
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
Descripción
Sumario: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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