Towards the development of a probabilistic approach to informal settlement fire spread using ignition modelling and spatial metrics

Abstract
ENGLISH ABSTRACT: Large conflagrations of informal settlements occur regularly, leaving thousands of people homeless daily and taking tens of thousands of lives annually. Over the past few years, a large amount of data has been collected from a number of full-scale informal settlement fire experiments. This paper uses that data with a semi-probabilistic fire model previously proposed by the authors, to illustrate the potential applications of the fire spread method proposed. The current model is benchmarked against a 20-dwelling full-scale informal settlement fire experiment, and the effects of the (a) ignition criteria, (b) wind direction, and (c) wind speeds on the predicted fire spread rates are investigated through the use of a parametric study. Colour maps of the fire spread rates and patterns are then used to visually interpret the effects of different types of fire scenarios and fire breaks. Finally, the fire spread capability within B-RISK is used to derive a linear equation for the potential fire spread rate as a function of the settlement spatial metrics (e.g., density and distance to nearest neighbour). To further illustrate the potential application of this work, the fire spread rate equation is then applied across the whole of Cape Town, South Africa, to show the 10 informal settlement areas most at “risk” of large conflagrations.
Description
CITATION: Cicione, A. et al. 2020. Towards the development of a probabilistic approach to informal settlement fire spread using ignition modelling and spatial metrics. Fire, 3(4):67, doi:10.3390/fire3040067.
The original publication is available at https://www.mdpi.com
Keywords
Informal settlements (Squatter settlements) -- Fires, Fire prevention, Spatial metrics, Fires -- Prevention and control
Citation
Cicione, A. et al. 2020. Towards the development of a probabilistic approach to informal settlement fire spread using ignition modelling and spatial metrics. Fire, 3(4):67, doi:10.3390/fire3040067.