Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabiliti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/9/1813 |
| _version_ | 1797535064384339968 |
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| author | Zechuan Wu Mingze Li Bin Wang Ying Quan Jianyang Liu |
| author_facet | Zechuan Wu Mingze Li Bin Wang Ying Quan Jianyang Liu |
| author_sort | Zechuan Wu |
| collection | DOAJ |
| description | Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment. |
| first_indexed | 2024-03-10T11:39:11Z |
| format | Article |
| id | doaj.art-2f1a2f0a765c44d8a1073bb743ef165b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:39:11Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2f1a2f0a765c44d8a1073bb743ef165b2023-11-21T18:35:29ZengMDPI AGRemote Sensing2072-42922021-05-01139181310.3390/rs13091813Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast ChinaZechuan Wu0Mingze Li1Bin Wang2Ying Quan3Jianyang Liu4Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaAlthough low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment.https://www.mdpi.com/2072-4292/13/9/1813forest managementHeilongjiang forest areafire predictionANN |
| spellingShingle | Zechuan Wu Mingze Li Bin Wang Ying Quan Jianyang Liu Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China Remote Sensing forest management Heilongjiang forest area fire prediction ANN |
| title | Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China |
| title_full | Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China |
| title_fullStr | Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China |
| title_full_unstemmed | Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China |
| title_short | Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China |
| title_sort | using artificial intelligence to estimate the probability of forest fires in heilongjiang northeast china |
| topic | forest management Heilongjiang forest area fire prediction ANN |
| url | https://www.mdpi.com/2072-4292/13/9/1813 |
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