ORIGINAL RESEARCH article
Front. For. Glob. Change
Sec. Fire and Forests
Volume 8 - 2025 | doi: 10.3389/ffgc.2025.1637263
This article is part of the Research TopicClimate Change, Forest Fire Risks, and Adaptation Strategies for Sustainable Ecosystem ManagementView all articles
Spatial-Temporal Distribution Prediction of Transmission Corridor Wildfire Risk Based on ARIMA-DBN
Provisionally accepted
- 1Laboratory of Power Equipment Reliability Enterprise, Electric Power Research Institute of Guangdong Power Grid Co., guangdong, China
- 2Shaoguan Power Supply Bureau, Guangdong Power Grid Co., Ltd, shaoguan, China
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This study proposes a predictive model for assessing the spatiotemporal risk of wildfire occurrence in transmission corridors, with an emphasis on the role of meteorological factors in short-term wildfire dynamics. A comprehensive set of 17 factors across four categories is considered. Following factor selection via the Random Forest (RF) algorithm, the predictive model is constructed using the key subset of wildfire factors. The Auto Regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) algorithms are employed to predict dynamic meteorological factor data, while the Dynamic Bayesian Network (DBN) is used to explore the interrelationships among wildfire factors across different time periods. The result shows that the DBN-based wildfire risk assessment model at a 3-day time scale achieves a high accuracy of 86.39%; when utilizing meteorological data predicted by ARIMA-GARCH, the wildfire risk prediction model still reaches an accuracy of 79.64%. Additionally, wildfire risk distribution maps for typical high-risk periods in Guangdong Province are generated using the model, revealing that 80.00%, 100.00%, 70.00%, and 72.72% of actual fire points, respectively, fall within high-risk and very high-risk areas, demonstrating the model's ability to provide accurate short-term predictions. This model offers significant value for decision-making in wildfire management, particularly for policymakers, grid operators, and fire management teams, enhancing the efficiency of risk mitigation efforts in critical transmission corridors.
Keywords: Wildfire, Risk Assessment, Dynamic Bayesian network, time series forecasting, Disaster Prevention and Mitigation for Power Systems
Received: 29 May 2025; Accepted: 14 Aug 2025.
Copyright: © 2025 Zhou, Wang, wang, wang and wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
ze en Zhou, Laboratory of Power Equipment Reliability Enterprise, Electric Power Research Institute of Guangdong Power Grid Co., guangdong, China
lei Wang, Shaoguan Power Supply Bureau, Guangdong Power Grid Co., Ltd, shaoguan, China
ning zi wang, Shaoguan Power Supply Bureau, Guangdong Power Grid Co., Ltd, shaoguan, China
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