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• W3159578922 endingPage "100862" @default.
• W3159578922 startingPage "100862" @default.
• W3159578922 abstract "Generalised Extreme Value (GEV) distribution can be used effectually to model extreme climatic events like bushfire. The major predicament of using GEV distribution is accurate determination of three parameters (location, scale, and shape); nevertheless, there are no specific guidelines to identify the most apposite parameter estimation technique of the GEV distribution for bushfire studies. In this study, influence of different GEV parameters estimation techniques were investigated in Victoria, Australia for extreme bushfire event modelling, using annual maximum forest fire danger index (FFDI), which is a combination of manifold climatic and fuel variables to indicate the potential for bushfires to propagate, and withstand suppression. Four GEV parameters estimation methods namely: Maximum Likelihood Estimation (MLE), Generalised Maximum Likelihood Estimation (GMLE), Bayesian and L-moments were used for two different timescale data (full data set and last 10 years of full dataset) to estimate the GEV distribution parameters. The return levels of FFDI for different Average Recurrence Interval (ARI) were estimated using the above mentioned four methods and two timescales. The study demonstrates that Fréchet (type II) extreme value distribution is pertinent for modelling the annual maximum FFDI for most of the selected stations; nonetheless, GEV distribution parameters can vary considerably due to variation in the length of the data series. Several applied statistical parameters namely: Mean square error (MSE) and Mean absolute error (MAE) were used to identify the most pertinent parameter estimation technique of the GEV distribution. The study reveals that L-moments can be used, even in the presence of the smaller data set. In addition, L-moments is the most appropriate parameters estimation technique of GEV distribution because of the presence of the lowest MSE and MAE values for most of the stations. The outcomes of this research are pivotal for Victorian public and private stakeholders to forecast the severity and intensity of imminent bushfire events due to recent bushfire events in fire prone areas." @default.
• W3159578922 created "2021-05-10" @default.
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• W3159578922 date "2021-05-01" @default.
• W3159578922 modified "2023-10-14" @default.
• W3159578922 title "Evaluation of different parameter estimation techniques in extreme bushfire modelling for Victoria, Australia" @default.
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• W3159578922 doi "https://doi.org/10.1016/j.uclim.2021.100862" @default.
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