Wildfire risk assessment using deep learning in Guangdong Province, China

Abstract

The severe wildfires that have ravaged Guangdong province, China, present a significant threat to the local ecosystem, socio-economics, and public health. Effective risk assessment is essential for early warning and timely prevention in wildfire management, thereby mitigating disaster losses. In this study, we compiled a dataset comprising 11,507 historical wildfire incidents in Guangdong Province spanning a decade (2011–2021) and developed a deep learning-based model to predict the likelihood of wildfire occurrence in the region. In addition to analyzing risk characteristics throughout the year, we also trained separate models for different seasons and analyzed the discrepancies in the contribution of driven factors to wildfire occurrence across seasons. Furthermore, the performance of our deep learning-based model was compared with that of traditional machine learning algorithms. The experimental results revealed that: (1) Factors such as relative humidity, temperature, NDVI, and precipitation exerted significant influence on wildfire occurrence. (2) The impact of wildfire driving factors varied across different seasons. (3) Our deep learning model outperformed traditional machine learning models, achieving a superior performance with an AUC of 0.962.