AbstractThis thesis will inform effective decision making in a natural disaster environment by combining positivist research data fully describing past disaster events, constructed into models that may assist in forecasting outcomes of future disaster events.
Typically, a vast amount of situational data from a particular natural disaster is collected and stored during the time band of the event. It is collected by participants such as emergency responders, government agencies and researchers.
The consequences of most natural disasters are the outputs arising from multiple inputs to a natural and anthropological system that are related through complex relationships. In this study these inputs, outputs and relationships are used to create transformation models. This study provides an original approach to physical data and information management, building initial representation models, and creating transformation models to assist decision making,
The thesis introduces a new dimensionless parameter that models relative human behaviour during pre-event and event time bands when potentially; behavioural responses are shown to affect the forecast outcomes based on measured situational data.
The internationally standardised tool for managing a risk or hazard is a two dimensional matrix of historical event likelihood, and the magnitude of consequences. Extending the traditional two-dimensional matrix to a three-dimensional matrix that includes a participant behavioural parameter is shown to inform more informative forecasting of disaster outcomes.
The study involves a research programme of one foundation study and three situational studies in montane environments that introduce new model approaches to risk management. The essential element of building this model is the use of a well posed, problem building principles to enable the creation of a structurally robust and solvable mathematical model.
The foundation study researches the historical development of data modelling and finds a structured set of seven archetypal forms of models from a catalogue of 2968 general models. These archetypal forms of models are applied to three different situational studies. The first situational study investigates the Gutenberg-Richter Equation as a reliable model for forecasting the likelihood of long-range seismic trends in the Snowy Mountain Region and the overlayed effects of Reservoir Induced Seismicity (RIS) amongst the 52 water dams in the greater Snowy Mountains Region. The study uses transformation models, to show how traditional investigations have over-reported the frequency and magnitude of RIS in this region. This new modelling approach provides a much improved RIS evaluation criteria, as well a surprising finding that reservoirs significantly reduce the risk of serious damage and harm from seismic events when they do, occasionally, occur.
The second situational study looks at the second major earthquake in the Canterbury, New Zealand sequence of 2010-11. This second of four strong and major earthquakes caused massive damage, 185 fatalities, and 2,000 moderate to serious injuries, mostly in the city of Christchurch. This study takes a new approach to the transformation modelling of damage using the attenuation of seismic energy to develop a new quantitative model called here the Specific Surface Energy (SSE). This new mathematical model now provides a quantitative definition based on measured seismic data for the historic Modified Mercalli Intensity (MMI) scale of seismic intensity. The study identifies several new seismic intensity anomalies that show significant geological features beyond the well-known Bexley liquefaction anomaly may lead to very different risks of damage and consequences. These outcomes may have significant consequences implications for the next major event on the NZ Alpine Fault.
The third situational study develops a new approach to studying and forecasting human behaviour in montane natural hazard situations by investigating recreational visitor and resident, understanding and responses to montane risks in the Snowy Mountains in NSW. The study shows, as might be expected, that visitors and residents will likely behave measurably different when confronted with montane natural hazard risks. The study models a new method of measuring differences in visitor and resident risk awareness that transforms into different measures of behaviour for application to natural hazard risk assessment models.
In the conclusion, the studies are synthesised into a mathematically robust, three domain matrix model where: natural hazard risk = likelihood * consequences * behaviour.
|Date of Award||2019|
|Supervisor||Ken Mcqueen (Supervisor), Robert Tanton (Supervisor), Tracey Dickson (Supervisor) & Bill Maher (Supervisor)|