Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area
Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed fa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/1/42 |
| _version_ | 1797431313108566016 |
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| author | Yuheng Li Shuxing Xu Zhaofei Fan Xiao Zhang Xiaohui Yang Shuo Wen Zhongjie Shi |
| author_facet | Yuheng Li Shuxing Xu Zhaofei Fan Xiao Zhang Xiaohui Yang Shuo Wen Zhongjie Shi |
| author_sort | Yuheng Li |
| collection | DOAJ |
| description | Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed factors affecting wildfire using the Random Forest (RF) model. No single factor played a decisive role in the incidence of wildfires. However, the climatic variables were most critical, dominating the occurrence of wildfires. The probability of wildfire occurrence was simulated and predicted using the Adaptive Network-based Fuzzy Inference System (ANFIS). The particle swarm optimization (PSO) model and genetic algorithm (GA) were used to optimize the ANFIS model. The hybrid ANFIS models performed better than single ANFIS for the training and validation datasets. The hybrid ANFIS models, such as PSO-ANFIS and GA-ANFIS, overcome the over-fitting problem of the single ANFIS model at the learning stage of the wildfire pattern. The high classification accuracy and good model performance suggest that PSO-ANFIS can be used to predict the probability of wildfire occurrence. The probability map illustrates that high-risk areas are mainly distributed in the northeast part of the study area, especially the grassland and forest area of Dornod Province of Mongolia, Buryatia, and Chita state of Russia, and the northeast part of Inner Mongolia, China. The findings can be used as reliable estimates of the relative likelihood of wildfire hazards for wildfire management in the region covered or vicinity. |
| first_indexed | 2024-03-09T09:42:06Z |
| format | Article |
| id | doaj.art-d84d1f6892d34162879cef735392a761 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:42:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d84d1f6892d34162879cef735392a7612023-12-02T00:50:39ZengMDPI AGRemote Sensing2072-42922022-12-011514210.3390/rs15010042Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border AreaYuheng Li0Shuxing Xu1Zhaofei Fan2Xiao Zhang3Xiaohui Yang4Shuo Wen5Zhongjie Shi6Research Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, ChinaResearch Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, ChinaSchool of Forestry and Wildlife Science, Auburn University, Auburn, AL 36830, USAResearch Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, ChinaResearch Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, ChinaResearch Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, ChinaResearch Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, ChinaWildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed factors affecting wildfire using the Random Forest (RF) model. No single factor played a decisive role in the incidence of wildfires. However, the climatic variables were most critical, dominating the occurrence of wildfires. The probability of wildfire occurrence was simulated and predicted using the Adaptive Network-based Fuzzy Inference System (ANFIS). The particle swarm optimization (PSO) model and genetic algorithm (GA) were used to optimize the ANFIS model. The hybrid ANFIS models performed better than single ANFIS for the training and validation datasets. The hybrid ANFIS models, such as PSO-ANFIS and GA-ANFIS, overcome the over-fitting problem of the single ANFIS model at the learning stage of the wildfire pattern. The high classification accuracy and good model performance suggest that PSO-ANFIS can be used to predict the probability of wildfire occurrence. The probability map illustrates that high-risk areas are mainly distributed in the northeast part of the study area, especially the grassland and forest area of Dornod Province of Mongolia, Buryatia, and Chita state of Russia, and the northeast part of Inner Mongolia, China. The findings can be used as reliable estimates of the relative likelihood of wildfire hazards for wildfire management in the region covered or vicinity.https://www.mdpi.com/2072-4292/15/1/42ANFISwildfireChina–Mongolia–Russia cross-border areagenetic algorithm (GA)particle swarm optimization (PSO)random forest |
| spellingShingle | Yuheng Li Shuxing Xu Zhaofei Fan Xiao Zhang Xiaohui Yang Shuo Wen Zhongjie Shi Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area Remote Sensing ANFIS wildfire China–Mongolia–Russia cross-border area genetic algorithm (GA) particle swarm optimization (PSO) random forest |
| title | Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area |
| title_full | Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area |
| title_fullStr | Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area |
| title_full_unstemmed | Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area |
| title_short | Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China–Mongolia–Russia Cross-Border Area |
| title_sort | risk factors and prediction of the probability of wildfire occurrence in the china mongolia russia cross border area |
| topic | ANFIS wildfire China–Mongolia–Russia cross-border area genetic algorithm (GA) particle swarm optimization (PSO) random forest |
| url | https://www.mdpi.com/2072-4292/15/1/42 |
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