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

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.