Modelling forest fire spread using hexagonal cellular automata

Abstract

In this paper a new mathematical model for predicting the spread of a fire front in homogeneous and inhomogeneous environments is presented. It is based on a bidimensional cellular automata model, whose cells stand for regular hexagonal areas of the forest. The results obtained are in agreement with the fire spreading in real forests.

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

“…Examples of CA applications include modelling population dynamics [51,152], animal migration [143], unsaturated flow [61], landscape changes [55], debris flow [39], earthquake activity [65], lava flow [37] and fire spread [75,89]. In these examples it is clear how the state of a cell is influenced by the previous state of its neighbours.…”

An optimal Cellular Automata algorithm for simulating wildfire spread

“…The work of Finney (2002) demonstrates how the use of an indefinitely large neighborhood leads to results equivalent to the ones given by a vector implementation, at the cost of larger computational costs and, more importantly, of large errors in the presence of real landscapes characterized by fuel and weather variability. Alternative discretizations have also been used: Frandsen and Andrews (1979) and Hern andez Encinas et al (2007) employed hexagonal cells suffering from the same distortion problems found in the square model, Johnston et al (2008) employed an irregular grid to avoid directional biasing in fire-front propagation. To improve the produced fire perimeter, they modified the rate of spread with the inclusion of two terms: the first one divides a region where the backing rate of spread is employed from one where a variable rate of spread is used, the latter increases the maximum rate of spread to allow for the fact that on an irregular grid fire cannot travel in a straight line, but follows an irregular path dictated by the cells' connections.…”

Understanding Current and Future Landuse Dynamics With Land Surface Temperature Alterations: A Case Study of Chandigarh

“…Assessment of LULC and prediction of likely changes would aid in formulating the strategies and policies for sustainable development in future in terms of housing, transportation (Schnebele et al, 2015), food (Wenban-Smith et al, 2016) and water security (Duh et al, 2008;Usali and Ismail, 2010), health and solid waste management (Li et al, 2012;Mundhe et al, 2017;Glanville and Chang, 2015). Researchers started investigating complex behavior of changing systems with the help of Cellular Automation (CA) in various scientific fields from medicine, forest fire modeling, urban modeling, landscape dynamics and so on (Couclelis, 1985;Torrens, 2000;Ermentrout and Edelstein, 1993;Hernández et al, 2007;Batty and Xie, 1994;Sante et al, 2010). SLEUTH model has evolved from CA model considering both spatial and temporal aspects of urban growth for simulation and prediction.…”