Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8349 |
| _version_ | 1827701526262448128 |
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| author | Wei-Chang Yeh Chia-Chen Kuo |
| author_facet | Wei-Chang Yeh Chia-Chen Kuo |
| author_sort | Wei-Chang Yeh |
| collection | DOAJ |
| description | The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study. |
| first_indexed | 2024-03-10T14:35:44Z |
| format | Article |
| id | doaj.art-3a19b5be99a34af68d78ef2c15105fd5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:35:44Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3a19b5be99a34af68d78ef2c15105fd52023-11-20T22:11:30ZengMDPI AGApplied Sciences2076-34172020-11-011023834910.3390/app10238349Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRankWei-Chang Yeh0Chia-Chen Kuo1Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu City 300, TaiwanIntegration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu City 300, TaiwanThe nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study.https://www.mdpi.com/2076-3417/10/23/8349wildfirefire modelmodel performancePageRankbinary-addition tree (BAT)states |
| spellingShingle | Wei-Chang Yeh Chia-Chen Kuo Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank Applied Sciences wildfire fire model model performance PageRank binary-addition tree (BAT) states |
| title | Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank |
| title_full | Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank |
| title_fullStr | Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank |
| title_full_unstemmed | Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank |
| title_short | Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank |
| title_sort | predicting and modeling wildfire propagation areas with bat and maximum state pagerank |
| topic | wildfire fire model model performance PageRank binary-addition tree (BAT) states |
| url | https://www.mdpi.com/2076-3417/10/23/8349 |
| work_keys_str_mv | AT weichangyeh predictingandmodelingwildfirepropagationareaswithbatandmaximumstatepagerank AT chiachenkuo predictingandmodelingwildfirepropagationareaswithbatandmaximumstatepagerank |