Abstract
Wildfire is one of the natural hazards that poses threats to the safety of forest ecological environment. It is very important to predict wildfire risk in the early stage. Most of the wildfire prediction research based on deep learning networks only extracts features on the spatial dimension. In this work, a deep learning model hybridizing 3D CNN and ConvLSTM (Convolutional Long short Term Memory) was proposed, where the strategies of multisource spatiotemporal cooperative feature fusion are adopted. Some redundant wildfire factors with high correlations by multiple collinear analysis and weight analysis were eliminated. Different from other methods, the daily weather forecast was used as the input of the study region, shortening the time prediction resolution from annual or quarterly to daily to achieve a more accurate prediction in time. Taking the daily ignition in Yunnan Province, China, as the research object, the experimental results showed that the proposed model performs well on the test dataset (AUC = 0.901 and accuracy = 0.912). Seven mainstream machine learning methods were employed for comparison with the proposed model. Ablation and comparison experiments show that the proposed model is a valid alternative tool for wildfire susceptibility prediction.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available because these data are the results of the author’s efforts and studies but are available from the corresponding author upon reasonable request.
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Funding
This work was supported in part by the National NSF of China under Grant 62262062, Grant 61872095, Grant 61571139, Grant 61872128, in part by Fujian Science Fund for Distinguished Young Scholars under Grant 2020J06043, in part by National Key RD Program of China 2021YFB2900900, in part by the Basic Public Welfare Research Plan Project of Zhejiang Province (No. LGG22F010011), and Startup Research Found Plan Project (No. BSYJ202107) funded by Quzhou University, China.
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Jiehang Deng: Conceptualization, Methodology, Supervision. Weiming Wang: Data curation, Writing- Original draft preparation, Validation. Guosheng Gu: Visualization, Investigation. Zhiqiang Chen: Funding acquisition, Writing - review & editing, Investigation. Jing Liu: Software, Data curation. Guobo Xie: Project administration. Shaowei Weng: Investigation. Lei Ding: Resources. Chuan Lie: Formal analysis. All authors read and approved the final manuscript.
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Communicated by Hassan Babaie.
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Deng, J., Wang, W., Gu, G. et al. Wildfire susceptibility prediction using a multisource and spatiotemporal cooperative approach. Earth Sci Inform 16, 3511–3529 (2023). https://doi.org/10.1007/s12145-023-01104-6
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DOI: https://doi.org/10.1007/s12145-023-01104-6