- Title
- Applicability of artificial neural network in hydraulic experiments using a new sewer overflow screening device
- Creator
- Aziz, Md Abdul; Imteaz, Monzur; Choudhury, Tanveer; Phillips, David
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160778
- Identifier
- vital:12253
- Identifier
- ISBN:1324-1583
- Abstract
- During wet weather conditions, sewer overflows to receiving water bodies raise serious environmental, aesthetic and public health problems. These issues trigger the need the most appropriate device/system for a particular installation, especially at unmanned remote locations. A new sewer overflow device consists of a rectangular tank and a sharp crested weir with a series of vertical combs is presented. A series of laboratory tests to determine trapping efficiencies for common sewer solids were conducted for different flow conditions, number of combs layers and spacing of combs. To overcome physical limitations inherent in laboratory studies such as significant cost and time. Artificial neural model was adopted as it has the capacity to accurately predict the outcome of complex, non-linear physical systems with relatively poorly understood physicochemical processes. A series of laboratory tests were conducted with 55 different sets of data. Forty-seven sets of experimental data are used with 60% for training, 20% each for testing and validation of the model. A separate validation data sets were used to judge the overall performance of the trained network. The model can successfully predict the experimental results with more than 90% accuracy with an average absolute percentage error of around 7%. © Institution of Engineers Australia, 2013.
- Publisher
- Taylor & Francis
- Relation
- Australian Journal of Water Resources Vol. 17, no. 1 (2013), p. 77-86
- Rights
- Copyright © Institution of Engineers Australia, 2013.
- Rights
- This metadata is freely available under a CCO license
- Subject
- MD Multidisciplinary; Artificial neural network (ANN); Capture efficiency; Screening device; Sewer solids
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