Wet weather sewer overflow 'Hotspot' site identification using a Bayesian Network model

Goulding, R, Jayasuriya, N and Horan, E 2011, 'Wet weather sewer overflow 'Hotspot' site identification using a Bayesian Network model', in Chathurika Hettiarachchi, Prabhashrini Dhanushika, Hasanthi Pathberiya (ed.) Proceedings of the International Statistics Conference 2011, Colombo, Sri Lanka, 28th to 30th December 2011, pp. 68-77.


Document type: Conference Paper
Collection: Conference Papers

Title Wet weather sewer overflow 'Hotspot' site identification using a Bayesian Network model
Author(s) Goulding, R
Jayasuriya, N
Horan, E
Year 2011
Conference name International Statistics Conference 2011: Statistical Concepts and Methods for the Modern World
Conference location Colombo, Sri Lanka
Conference dates 28th to 30th December 2011
Proceedings title Proceedings of the International Statistics Conference 2011
Editor(s) Chathurika Hettiarachchi, Prabhashrini Dhanushika, Hasanthi Pathberiya
Publisher Institute of Applied Statistics
Place of publication Colombo, Sri Lanka
Start page 68
End page 77
Total pages 10
Abstract Wet weather sewer overflows arise from sanitary sewer systems when the hydraulic capacity of the sewerage system is exceeded due to entry of rainfall into the sewer via inflow and infiltration. Wet weather sewer overflows are considered a potential threat to the ecological and public health of the waterways which receive these overflows. Due to variability in sewer overflow events and subsequent impacts, it is currently difficult for water retailers or responsible authorities to extrapolate findings from existing studies to their own unique situations. This paper presents a Bayesian network model to assess the public health risk associated with wet weather sewer overflows. Through an application of probabilistic inference (scenario analysis), the Bayesian network model is used to identify 'hotspots' or 'worst-case' sites where conditions are such that the highest risk to waterway values. This demonstrates how the Bayesian network approach can be used to represent various sites or situations, and in particular to identify priority sites for attention. In addition, scenario analysis is used to determine the degree of effectiveness of various sewer overflow management options in reducing risk outcomes at the worst-case site. It is argued that the Bayesian network model developed in this research will be useful to water retail companies and other responsible authorities in prioritising management options to minimise public health risks from sewer overflow.
Subjects Water Quality Engineering
Environmental Engineering not elsewhere classified
Keyword(s) Sanitory sewer overflows
Bayesian networks
Public health
Risk assessment
Copyright notice © Institute of Applied Statistics, Sri Lanka
ISBN 9789550056019
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