- Title
- Accurate prediction of sewer network flows and assessment of groundwater infiltration volumes in sewer networks
- Creator
- Jayasooriya, Mahinda
- Date
- 2022
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/195115
- Identifier
- vital:18476
- Abstract
- Excessive flows due to inflow and infiltration (I&I) in sewer networks contribute to hydraulically overloaded sanitary sewer networks. Understanding the cause–effect relationships associated with sewer network flow generation and accurate quantification of excessive flow volumes entering sewer networks is crucial to developing solutions to resolve this issue. A systematic literature review found a gap in understanding the cause–effect relationships of I&I and no universally accepted method for I&I estimation. This research used three years of data from an Australian sewer catchment in Ballarat, Victoria, as a case study to enhance understanding of the network flow generation process. The aims of this research were to accurately predict sewer network flows, reliably separate the groundwater infiltration (GWI) volume of sewer network flow, improve understanding of associated processes and identify the influential parameters that impact sewer network flow. This research used three methods to achieve these aims. First, commercially available software was used to develop and calibrate a sewer network model to predict flows with data from the case study catchment. The best practice guidelines available in Australia were used to calibrate the model, and a new method was introduced to separate the sewer network flow components. Second, two classical hydrology applications, the recursive filter and the flow duration curve (FDC) methods, were used to separate the GWI component of the total sewer network flow and understand the performance of the sewer catchments. Third, an artificial neural network (ANN) model was developed to predict sewer network flow. Parameters known to influence sewer flows were incrementally introduced to the ANN model to improve performance and understand their relative importance. This study finds that commercially available software can predict total sewer network flows with reasonable accuracy using the Wallingford or triangular hydrograph (RTK) hydrological implementation methods. However, the software cannot replicate the dynamic nature of the complex I&I processes that occur over time or reliably separate the sewer network flow components. A key finding from this study is that commercially available software has clear limitations in separating sewer network flow components. Recursive filter and FDC methods can be successfully used as standalone techniques to calculate the GWI volume. These methods have further value in comparing the I&I between catchments to determine flow characteristics and are straightforward for practising engineers to use. The ANN model predicts sewer flow with a high level of accuracy, and soil moisture is the most critical predictor of the total flow regime. The ANN model is also able to predict the dynamic and complex nature of I&I into sewer networks. More research is needed to achieve effective and reliable separation of individual components in an overall sewer flow regime. ANN and other emerging machine learning techniques show much potential to effectively predict sewer flows across a range of sewer network and catchment characteristics.; Doctor of Philosophy
- Publisher
- Federation University Australia
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Mahinda Jayasooriya
- Rights
- Open Access
- Subject
- Sewer networks,; Inflow; Infiltration; Ground water infiltration; Flow prediction
- Full Text
- Thesis Supervisor
- Barton, Andrew
- Hits: 1798
- Visitors: 1108
- Downloads: 52
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE2 | Australian Digital Thesis | 3 MB | Adobe Acrobat PDF | View Details Download |