Abstract
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.
Highlights
Fire is a natural process that has an essential contribution to the ecosystem
It was mentioned that the majority of the building structures in the target areas were all residential and the type of construction materials used was generally light
A comparison of the features in all study areas and a summary of the features for both the training data and testing data is shown in Table 2 and Table 3
Summary
Fire is a natural process that has an essential contribution to the ecosystem. Different mitigation and preventive measures have been established to control its negative effects. It is important to develop these measures as they could serve as the basis for devising plans for future firefighting activities. Various methods have been introduced as tools for formulating these strategies, such as fire danger estimation systems (Vasilakos et al, 2009), fire loss assessment systems (Zhao, 2011), and fire spread modelling and simulation systems (Bertinshaw, Guesgen, 2002). Virtual simulation of the spread of the fire was considered since by many researchers as an integral instrument for the fire departments to create effective plans for fire disaster mitigation
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