Spatial-temporal distribution prediction of transmission corridor wildfire risk based on ARIMA-DBN

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.