Leveraging Predictive Analytics for Early Warning Detection Result: TasWater, Tasmania’s primary water and sewerage network provider, manages more than 200,000 water connections and 176,000 sewerage connections across the state. Following a sewer spill at an environmentally significant site at Midway Point in August 2017, TasWater sought a way to reduce the likelihood and impact of spill events occurring in the future.
Challenge: Managing sewer blockages and subsequent spills that occur in the mains system was difficult and often the first notification of a spill came from a member of the public, hours and sometimes days after the first spill, intensifying public health and environmental impacts and the cost of clean-up.
Solution: Build a sewer blockage detection system by developing a model to detect the blockage faster than current methods.
Result: The model detected a historical blockage 13 hours before the time it was reported by a customer, without false positives, and will help to avoid spill incidents by providing real-time early warning for field teams of statistically significant partial or full blockages in the sewer network.
A model was developed and tested using data from the Midway Point spill event in August 2017. The goal was to see if the model could detect the blockage faster than current methods. The model identifies blockages by detecting the absence of ‘normal fill and pump behavior’ in real-time. For example, the absence of pump runs or extended fill time during peak times would signify an abnormality and possible blockage. Using the August 2017 blockage event, the model was validated as an accurate, reliable and fast method of identifying potential blockages. Given its superior performance, the on-peak/off-peak model was selected for the online sewer blockage detection system and applied to each SPS in the Midway Point region. Each site’s specific behavior, deviations and grace periods were accounted for to reduce false positives. The solution is integrated with PI notifications for early alerting, allows for fast time-to-insight on time-series data, reduced time spent in spreadsheets, and process improvements leading to better operating efficiencies.
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