Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment
The quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/13/12/1974 |
| _version_ | 1797458591537430528 |
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| author | Yiqing Xu Dianjing Li Hao Ma Rong Lin Fuquan Zhang |
| author_facet | Yiqing Xu Dianjing Li Hao Ma Rong Lin Fuquan Zhang |
| author_sort | Yiqing Xu |
| collection | DOAJ |
| description | The quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it is therefore used to model the complex process of forest fire spread. However, the process of forest fire spread is linked with a variety of mutually influencing factors, which are too complex to analyze using conventional approaches. Here, we propose a new method for modeling fire spread, namely LSSVM-CA, in which least squares support vector machines (LSSVM) is combined with a three-dimensional forest fire CA framework. In this approach, the effects of adjacent wind on the law of fire spread are considered and analyzed. The LSSVM is utilized to derive the complex state transformation rules for fire spread by training with a dataset based on actual local data. To validate the proposed model, the forest fire spread area simulated by LSSVM-CA and the actual extracted forest fire spread area were subjected to cross-comparison. The results show that LSSVM-CA performs well in simulating the spread of forest fire and determining the probability of forest fire. |
| first_indexed | 2024-03-09T16:39:19Z |
| format | Article |
| id | doaj.art-990afaf1040c4326a24c2c5a9386d2be |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T16:39:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-990afaf1040c4326a24c2c5a9386d2be2023-11-24T14:53:12ZengMDPI AGForests1999-49072022-11-011312197410.3390/f13121974Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS EnvironmentYiqing Xu0Dianjing Li1Hao Ma2Rong Lin3Fuquan Zhang4Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department, Nanjing Vocational University of Industry Technology, Nanjing 210023, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaIndustrial Software Engineering Technology Research and Development Center of Jiangsu Education Department, Nanjing Vocational University of Industry Technology, Nanjing 210023, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaThe quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it is therefore used to model the complex process of forest fire spread. However, the process of forest fire spread is linked with a variety of mutually influencing factors, which are too complex to analyze using conventional approaches. Here, we propose a new method for modeling fire spread, namely LSSVM-CA, in which least squares support vector machines (LSSVM) is combined with a three-dimensional forest fire CA framework. In this approach, the effects of adjacent wind on the law of fire spread are considered and analyzed. The LSSVM is utilized to derive the complex state transformation rules for fire spread by training with a dataset based on actual local data. To validate the proposed model, the forest fire spread area simulated by LSSVM-CA and the actual extracted forest fire spread area were subjected to cross-comparison. The results show that LSSVM-CA performs well in simulating the spread of forest fire and determining the probability of forest fire.https://www.mdpi.com/1999-4907/13/12/1974cellular automaton (CA)machine learningforest fire simulatingleast squares support vector machines (LSSVM)three-dimensional |
| spellingShingle | Yiqing Xu Dianjing Li Hao Ma Rong Lin Fuquan Zhang Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment Forests cellular automaton (CA) machine learning forest fire simulating least squares support vector machines (LSSVM) three-dimensional |
| title | Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment |
| title_full | Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment |
| title_fullStr | Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment |
| title_full_unstemmed | Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment |
| title_short | Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment |
| title_sort | modeling forest fire spread using machine learning based cellular automata in a gis environment |
| topic | cellular automaton (CA) machine learning forest fire simulating least squares support vector machines (LSSVM) three-dimensional |
| url | https://www.mdpi.com/1999-4907/13/12/1974 |
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