Fire spread model for old towns based on cellular automaton

Gao, Nan; Weng, Wenguo; Ma, Wei; Ni, Shunjiang; Huang, Quanyi; Yuan, Huang

doi:10.1016/s1007-0214(08)70094-4

Cited by 8 publications

(18 citation statements)

References 3 publications

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“…A variety of such methods is reported in the literature [33,34,35,36,37,38,39]. However, until now such technologies have been applied mostly in higher-income countries, which have the resources and technical capacities to utilise them, and where relevant datasets (such as census and housing) are extensive, reliable, and publicly available.…”

Section: Fire Data Limitations and Challengesmentioning

confidence: 99%

Improved Methods for Fire Risk Assessment in Low-Income and Informal Settlements

Twigg

Christie

Haworth

et al. 2017

IJERPH

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Fires cause over 300,000 deaths annually worldwide and leave millions more with permanent injuries: some 95% of these deaths are in low- and middle-income countries. Burn injury risk is strongly associated with low-income and informal (or slum) settlements, which are growing rapidly in an urbanising world. Fire policy and mitigation strategies in poorer countries are constrained by inadequate data on incidence, impacts, and causes, which is mainly due to a lack of capacity and resources for data collection, analysis, and modelling. As a first step towards overcoming such challenges, this project reviewed the literature on the subject to assess the potential of a range of methods and tools for identifying, assessing, and addressing fire risk in low-income and informal settlements; the process was supported by an expert workshop at University College London in May 2016. We suggest that community-based risk and vulnerability assessment methods, which are widely used in disaster risk reduction, could be adapted to urban fire risk assessment, and could be enhanced by advances in crowdsourcing and citizen science for geospatial data creation and collection. To assist urban planners, emergency managers, and community organisations who are working in resource-constrained settings to identify and assess relevant fire risk factors, we also suggest an improved analytical framework based on the Haddon Matrix.

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Section: Fire Data Limitations and Challengesmentioning

confidence: 99%

Improved Methods for Fire Risk Assessment in Low-Income and Informal Settlements

Twigg

Christie

Haworth

et al. 2017

IJERPH

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“…The main focus of this study is to implement the existing algorithms for urban fire spread simulation using cellular automaton and integrate machine learning techniques in its implementation. Essentially, fire spread will be simulated using cellular automaton using wind speed and direction following the work of Ohgai et al (2004) and Gao et al (2008). The probability of the cell catching fire is estimated using machine learning of houses/buildings' properties.…”

Section: Methodsmentioning

confidence: 99%

“…Also, the studies mentioned several parameters that can prove useful when modelling a real urban area. Examples of these parameters are building materials, building height, building density, cell size, etc (Takizawa, 2000;Ohgai et al, 2004;Gao et al, 2008). Therefore, this study shall refer specifically to the works of Ohgai et al (2004) and Gao et al (2008) together with the work of Zheng et al (2016) to create a representation for urban areas.…”

Section: Urban Fire Spread Modelsmentioning

confidence: 99%

“…In this study, the works of Ohgai et al (2004) and Gao et al (2008) were considered as the blueprint of the model but instead of manually calculating the ignition probability of each cell, a data-driven approach was used. Furthermore, the overall process of the model could be described by the following flowchart: Figure 2.…”

Section: Fire Spread Simulation and Ignitability Modellingmentioning

confidence: 99%

“…Furthermore, the overall process of the model could be described by the following flowchart: Figure 2. Fire Spread Simulation Flow Chart 2.2.1 Characteristics of the Cell: Gao et al (2008) mentioned that there are three main factors that can affect the spread of fire in an urban area. Therefore, these factors were considered as the attributes of a cell in the cellular automaton: 3.…”

Section: Fire Spread Simulation and Ignitability Modellingmentioning

confidence: 99%

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Urban Fire Spread Modelling and Simulation Using Cellular Automaton With Extreme Learning Machine

Patac

Vicente

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban fire continues to be a persistent disaster, especially with the proliferation of highly dense urban settlements. As a response, several measures were established to help mitigate the losses caused by fire including simulating the fire spread. The cellular automaton system has been widely used to simulate the complex process of fire development along with Physics-based models. A data-driven approach has been rarely employed. This paper presents the result of incorporating machine learning techniques to the existing cellular automaton based urban fire spread models. Specifically, instead of manually calculating the ignition probability of each cell in the automaton, the Extreme Learning Machine (ELM) was used to learn the ignition probability from the historical data. After building the model, its performance was evaluated using the data collected from the four fires in Basak, Lapu-Lapu City. By using a confusion matrix to compare the actual and the predicted values, the Burned Actual – Burned Predicted relationship was derived. Results suggest that the proposed method can effectively describe the development of fire, and the model accuracy is quite good (i.e., the Burned Actual - Burned Predicted relationship ranges from 78% to 83%). Lastly, the study was able to demonstrate the possibility of using a data-driven approach in creating a simple cellular automaton fire spread simulation model for urban areas. Further studies utilizing more fire incident data on with varying properties is recommended.

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