Predicting the Damage of Urban Fires with Grammatical Evolution
Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cog...
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MDPI AG
2025-05-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/6/142 |
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| author | Constantina Kopitsa Ioannis G. Tsoulos Andreas Miltiadous Vasileios Charilogis |
| author_facet | Constantina Kopitsa Ioannis G. Tsoulos Andreas Miltiadous Vasileios Charilogis |
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| description | Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. |
| format | Article |
| id | doaj-art-c54f4e655f284c02b1f189ceb0ab566d |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-c54f4e655f284c02b1f189ceb0ab566d2025-08-20T03:32:31ZengMDPI AGBig Data and Cognitive Computing2504-22892025-05-019614210.3390/bdcc9060142Predicting the Damage of Urban Fires with Grammatical EvolutionConstantina Kopitsa0Ioannis G. Tsoulos1Andreas Miltiadous2Vasileios Charilogis3Department of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, GreeceFire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion.https://www.mdpi.com/2504-2289/9/6/142urban firesmachine learningneural networksgenetic programminggrammatical evolution |
| spellingShingle | Constantina Kopitsa Ioannis G. Tsoulos Andreas Miltiadous Vasileios Charilogis Predicting the Damage of Urban Fires with Grammatical Evolution Big Data and Cognitive Computing urban fires machine learning neural networks genetic programming grammatical evolution |
| title | Predicting the Damage of Urban Fires with Grammatical Evolution |
| title_full | Predicting the Damage of Urban Fires with Grammatical Evolution |
| title_fullStr | Predicting the Damage of Urban Fires with Grammatical Evolution |
| title_full_unstemmed | Predicting the Damage of Urban Fires with Grammatical Evolution |
| title_short | Predicting the Damage of Urban Fires with Grammatical Evolution |
| title_sort | predicting the damage of urban fires with grammatical evolution |
| topic | urban fires machine learning neural networks genetic programming grammatical evolution |
| url | https://www.mdpi.com/2504-2289/9/6/142 |
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