Assessment of fire danger vulnerability using McArthur’s forest and grass fire danger indices

Anwar

2022

Meteorol Atmos Phys

Self Cite

Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.

Section: Introductionmentioning

confidence: 99%

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Anwar

2022

Meteorol Atmos Phys

Self Cite

“…(2017) noted that mean temperature and relative humidity during summer months (Dec-Feb) are the most critical parameters for the occurrence of frequent bushfire events in Victoria. Even in Victoria, northwest Victoria is more fire prone compared to other parts of Victoria, because of high temperature and relatively low relative humidity (RH) (Khastagir et al 2018). Past bushfires in Victoria have damaged land, farm animals and property of the communities and in some instances killed people (Khastagir, 2013).…”

Section: Introductionmentioning

confidence: 99%

Bushfire prediction in Victoria, Australia using Generalized extreme value distribution

Aktar

2022

Preprint

Self Cite

To facilitate appropriate design, management, and improvement of infrastructures for both rural and urban communities, the prediction for the occurrence of future extreme climatic events e.g. bushfire is intrinsic. Widely used extreme climatic event prediction method, Generalized Extreme Value (GEV), was used in this study in predicting annual peak Forest Fire Danger Index (FFDI), which is a measure of fire behaviour used in different parts of Australia. The GEV distribution was fitted for 17 selected stations spreading all around Victoria, Australia. The estimation of the parameters for the GEV distribution was performed using Maximum Likelihood Estimation (MLE) method. Three goodness of fit tests such as: Kolmogorov-Smirnov (KS) test, Anderson-Darling (AD) test, and Chi-square test result were used to verify the efficacy of GEV distribution. Fire frequency curves (FFCs) were derived for Victoria to identify fire prone regions. The study revealed that Fréchet (type II) extreme value distribution is suitable for modelling the annual peak of FFDI for most of the selected stations. Annual peak FFDI for the 100 years return level (time average of every 100 years) can vary from 34 (Dartmouth) i.e. high fire danger event to 146 (Walpeup) i.e. catstrophic bushfire event. The study noted that three stations in Victoria namely: Nhill, Walpeup and Ouyen are vulnerable to catastrophic fire danger situation (FFDI ≥100) with at least 1% probability of occurrence (1 in 100 year). The developed FFCs for Victoria will guide key public and private stakeholders namely: catchment management authorities, insurance companies, structural designers, traffic modellers, and forest hydrologists when designing and managing infrastructures in fire prone areas.

“…The extreme value statistics projects the occurrence of future extreme rainfall through frequency analysis of the previous data (El Adlouni et al 2007). Frequency analysis is performed for a series of previous observations to fit statistical probability distributions (Khaliq et al 2006;Khastagir 2018;Khastagir et al 2018Khastagir et al , 2021Hossain et al 2021). Although several probability distribution functions can be used for the frequency analysis of extreme rainfall, generalised extreme value (GEV) is commonly used in extreme rainfall analysis.…”

Section: Introductionmentioning

confidence: 99%

Comparison of estimation techniques for generalised extreme value (GEV) distribution parameters: a case study with Tasmanian rainfall

Int. J. Environ. Sci. Technol.

Aktar

et al. 2021

Generalised extreme value (GEV) distribution is traditionally applied to model extreme event and their return period. There are three parameters (location, scale and shape) in GEV distribution, which needs to be determined before its application. Different techniques have been developed to estimate the parameters of the GEV distribution. There is no specific guidance regarding the optimal method for estimating the parameters of the GEV distribution. This paper investigated the sensitivity of different parameters estimation techniques which are being commonly used in the application of the GEV distribution. Stationary GEV was adopted for the homogeneous data sets; whereas, non-stationarity GEV was implemented for the nonhomogeneous data sets. Four methods were applied in the estimation of the GEV distribution parameters for four different timescales. The methods were applied in extreme rainfall modelling using extreme rainfall data in Tasmania, Australia as a case study. It was found that adoption of any GEV parameter estimation methods does not change the GEV type in Tasmanian extreme rainfall. The length of the data series has significant influence on the values of the GEV distribution parameters. The Fréchet type GEV distribution is suitable in most of the analysed rainfall stations in Tasmania.