부산대학교 도서관

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.