Dynamic Wildfire Navigation System

Abstract

Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 km 2 in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance.

Impact of Vertical Atmospheric Structure on an Atypical Fire in a Mountain Valley

“…Firstly, cross-section diagrams were employed to identify the conducive wind structures, which are defined as the combination of wind and temperature layers likely to result in enhanced surface wind, by profiling the atmosphere vertically to confirm the association of ruggedness with an upper air interaction. Secondly, fire spread simulations under a range of modelled wind fields were carried out with Prototype 2, the successor of the fire simulator, Prototype 1 [33]. The core fire models embedded in Prototype 2 are those in the Australian Fire Danger Rating System (AFDRS) with some modification.…”

“…Fire danger rating systems are helpful to communicate with the corresponding fire communities such as researchers and operators to share their awareness of fires [26]. Prototype 2 is a successor to the previous Prototype 1 model, which employed three polygonbased geometries with three granularities of prediction polygons, and was previously demonstrated to predict the fire spread in the 2016 Lake Mackenzie fire in Tasmania [33]. The Prototype 2 also follows the specification of the Australian Fire Danger Rating System (AFDRS) in terms of Australian fuel sub-models and was applied here to the Riveaux Road fire.…”

“…There are several geometries available, such as Delaunay, diamond, hexagon, square and Voronoi, to map the prediction of fire and the reason for employing various geometries is to provide a range of samplings of fire propagation to increase the prediction accuracy. Note that a granularity of the prediction polygon was not always associated with accuracy in earlier work [33]. In addition, each prediction spatial polygon consists of various properties, such as the burn completion status and an elapsed time from the ignition time.…”

Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

“…Prototype 2 is a fire simulator that incorporates various fire models of fire spread for different vegetation types and provide two types of wind field: BARRA-TA and downscaled winds (Ozaki et al, 2019). The prototype 2 then maps simulation results on either of five geometries: Delaunay, diamond, hexagon, square and Voronoi (Ozaki et al, 2022).…”

An evaluation of wildland fire simulators used operationally in Australia

“…Over the last two decades in particular, fire behaviour simulators, which model the spread and some other properties of fires, have become a tool to predict fire behaviour for that management (Finney 2004;Opperman et al 2006;Haas et al 2013). Simulator applications include estimating the likelihood of fire ignitions becoming difficult to control (Finney et al 2011), predicting direction and rate of growth of going fires (Tymstra et al 2010), assessing the impact of fuel reduction strategies on future fires (Ager et al 2010;State Fire Management Council 2014) and identifying safe evacuation routes from wildfires (Ozaki et al 2019). Owing to their quite recent introduction, wildland fire simulators lack standardisation, and new simulator versions have sometimes been introduced to operations without clear and comprehensive assessment of their suitability.…”