@article {bnh-7106, title = {The Australian Natural Disaster Resilience Index: Volume II {\textendash} Chapter 6: Uncertainty and sensitivity analysis}, number = {493}, year = {2020}, month = {07/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In this chapter

Section 6.1 Explains the role of uncertainty and sensitivity analysis in composite index construction.

Section 6.2 Describes the uncertainty analysis applied to the Australian Natural Disaster Resilience Index.

Section 6.2 Describes the sensitivity analysis applied to the Australian Natural Disaster Resilience Index.

}, keywords = {analysis, ANDRI, computation, disaster resilience, index design, sensitivity, uncertainty}, isbn = { 978-0-6482756-2-6}, issn = {493}, author = {Melissa Parsons and Ian Reeve and James McGregor and Graham Marshall and Richard Stayner and Judith McNeill and Peter Hastings and Sonya Glavac and Phil Morley} } @article {bnh-7343, title = {Effect of weather forecast errors on fire growth model projections}, journal = {International Journal of Wildland Fire}, year = {2020}, month = {08/2020}, abstract = {

Fire management agencies use fire behaviour simulation tools to predict the potential spread of a fire in both risk planning and operationally during wildfires. These models are generally based on underlying empirical or quasi-empirical relations and rarely are uncertainties considered. Little attention has been given to the quality of the input data used during operational fire predictions. We examined the extent to which error in weather forecasts can affect fire simulation results. The study was conducted using data representing the State of Victoria in south-eastern Australia, including grassland and forest conditions. Two fire simulator software packages were used to compare fire growth under observed and forecast weather. We found that error in the weather forecast data significantly altered the predicted size and location of fires. Large errors in wind speed and temperature resulted in an overprediction of fire size, whereas large errors in wind direction resulted in an increased spatial error in the fire{\textquoteright}s location. As the fire weather intensified, fire predictions using forecast weather under predicted fire size, potentially resulting in greater risks to the community. These results highlight the importance of on-ground intelligence during wildfires and the use of ensembles to improve operational fire predictions.

}, keywords = {Bayesian Network, fire prediction, meteorological forecast, sensitivity, simulation}, doi = {https://doi.org/10.1071/WF19199}, url = {https://www.publish.csiro.au/wf/wf19199}, author = {Trent Penman and Dan Ababei and Jane Cawson and Brett Cirulis and Thomas Duff and Swedosh, W and James Hilton} } @mastersthesis {bnh-5780, title = {The sensitivity of the empirical mode decomposition and its application on environmental data}, volume = {Doctor of Philosophy}, year = {2019}, month = {08/2019}, school = {The University of New South Wales}, type = {Doctorate}, address = {Sydney}, abstract = {

The Empirical Mode Decomposition (EMD) is a powerful data analysis method that can be used to extract embedded components within time series and other data. In the context of EMD, these embedded components are called Intrinsic Mode Functions (IMF). Since its inception in the late 1990s, EMD has been applied in a number of areas, including biomedicine, neuroscience, epidemiology, chemical engineering, finance, atmospheric turbulence, seismology and oceanography. The method is advantageous in that it is able to analyse nonlinear and non-stationary data. Surprisingly, the literature exploring the ability of EMD to analyse data with different types of non-stationarity is relatively sparse. Also, the sensitivities associated with the critical initial steps of the EMD procedure are not well understood. One of the critical steps in determining each of the IMFs involves constructing upper and lower envelopes of the local maxima and minima of the time series. In the original presentation of the EMD methodology, cubic-spline interpolation was used to construct these envelopes. However, there is now a prior reason to support the use of cubic splines, and it is natural to wonder how employing alternative interpolation methods might affect the ultimate outcome of the EMD method.This dissertation is dedicated to providing a more comprehensive understanding ofthe sensitivity of EMD to different interpolation methodologies, and to different types of data non-stationarity. These sensitivities are investigated systematically using synthetic time series data that cover a range of interesting features. In addition,a number of environmental data sets are used to explore how EMD sensitivity can influence the inferences that might be drawn from IMFs extracted using various forms of the EMD method. In this part of the study, temperature, sea level and forest fire danger rating time series are chosen. There is significant interest in the way these particular variables might be changing over various time scales, and how these changes might impact various ecosystems, including human societies. The analyses conducted in this thesis suggest that the robustness and accuracy of EMD is improved when smoothing-spline interpolation is employed as its underlying interpolation method. In particular, Smoothing Ensemble EMD (SEEMD) is introduced, and is shown to out-perform other EMD methods in a number of important contexts. Specifically, SEEMD is shown to be more robust in the presence of noise and is able to extract more meaningful features from the environmental times series. It is also found, however, that all EMD-based methods perform poorly when confronted with time series exhibiting abrupt non-stationarity.

}, keywords = {emergency management., empirical mode decomposition, environmental data, interpolation, sensitivity}, url = {https://www.unsworks.unsw.edu.au/primo-explore/fulldisplay?docid=unsworks_54791\&context=L\&vid=UNSWORKS\&lang=en_US\&search_scope=unsworks_search_scope\&adaptor=Local\%20Search\%20Engine\&tab=default_tab\&query=any,contains,the\%20sensitivity\%20of\%20the\%20empirica}, author = {Mona Ziaeyan Bahri} }