Predictive analysis of fire frequency based on daily temperatures

Frequent fires can affect ecosystems and public safety. The occurrence of fires has varied with hot and cold months in China. To analyze how temperature influences fire frequency, a fire dataset including 20,622 fires and a historical weather dataset for Changsha in China were gathered and processed. Through data mining, it was found that the mean daily fire frequency tended to be the lowest in the temperature range of (20 °C, 25 °C] and should be related to the low utilization rate of electricity. Through polynomial fitting, it was found that the prediction performance using the daily minimum temperature was generally better than that using the daily maximum temperature, and a quadruplicate polynomial model based on the mean daily minimum temperature of 3 days (the day and the prior 2 days) had the best performance. Then, a temperature-based fire frequency prediction model was established using quadruplicate polynomial regression. Moreover, the results are contrary to the content stipulated in China’s national standard of urban fire-danger weather ratings GB/T 20487-2006. The findings of this study can be applied as technical guidance for fire risk prediction and the revision of GB/T 20487-2006.