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Matthew Scorah

Population at Risk (PAR) estimation involves quantification of people who could be exposed to flooding in the event of a dam failure. Conventionally, estimates of PAR involve manual and subjective assessment of individual structures located downstream of dams. To reduce the reliance on subjective judgement and better leverage publicly available population datasets, an automated method of PAR assessment was developed. This approach used the Geoscape dataset of building representations to disaggregate Australian Bureau of Statistics 2016 Census data for a study area around Gawler, South Australia.

Representative day and night spatial distributions of PAR were constructed to characterise the diurnal movement of people between homes and workplaces or other day activities. Flows of people were directly quantified to reduce reliance on high level assumptions regarding exposure. A Random Forest model was used to filter sheds and other unpopulated structures from the Geoscape dataset.
The largest deficiency in this approach is the lack of high detail data to classify building usage. It is recommended that the potential for automation of PAR assessment be continually revisited as more datasets become available.

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