Prediction of Forest-Fire Occurrence in Eastern China Utilizing Deep Learning and Spatial Analysis

文献类型： 外文期刊

第一作者： Li, Jing

作者： Li, Jing;Liu, Yu;Wang, Jinwang;Li, Jing;Chen, Chuxiang;Huang, Duan;Huang, Duan;Shao, Yakui;Wang, Aiai;Li, Xusheng

作者机构：

关键词： forest-fire prediction; spatial distribution; East China forest fires; GIS integration; deep learning; kernel density estimation

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 15 卷 9 期

页码：

收录情况： SCI

摘要： Forest fires are a major natural calamity that inflict substantial harm on forest resources and the socio-economic landscape. The eastern region of China is particularly susceptible to frequent forest fires, characterized by high population density and vibrant economic activities. Precise forecasting in this area is essential for devising effective prevention strategies. This research utilizes a blend of kernel density analysis, autocorrelation analysis, and the standard deviation ellipse method, augmented by geographic information systems (GISs) and deep-learning techniques, to develop an accurate prediction system for forest-fire occurrences. The deep-learning model incorporates data on meteorological conditions, topography, vegetation, infrastructure, and socio-cultural factors to produce monthly forecasts and assessments. This approach enables the identification of spatial patterns and temporal trends in fire occurrences, enhancing both the precision and breadth of the predictions. The results show that global and local autocorrelation analyses reveal high-incidence areas mainly concentrated in Guangdong, Fujian, and Zhejiang provinces, with cities like Jiangmen exhibiting distinct concentration characteristics and a varied spatial distribution of fire occurrences. Kernel density analysis further pinpoints high-density fire zones primarily in Meizhou, Qingyuan, and Jiangmen in Guangdong Province, and Dongfang City in Hainan Province. Standard deviation ellipse and centroid shift analysis indicate a significant northward shift in the fire-occurrence centroid over the past 20 years, with an expanding spatial distribution range, decreasing flattening, and relatively stable fire-occurrence direction. The model performs effectively on the validation set, achieving an accuracy of 80.6%, an F1 score of 81.6%, and an AUC of 88.2%, demonstrating its practical applicability. Moreover, monthly fire zoning analysis reveals that high-incidence areas in spring and winter are mainly concentrated in Guangdong, Fujian, Zhejiang, and Hainan, while autumn shows widespread medium-incidence areas, and summer presents lower fire occurrences in most regions. These findings illustrate the influence of seasonal climate variations on fire occurrences and highlight the necessity for enhanced fire monitoring and prevention measures tailored to different seasons.

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