Temperature-based fire frequency analysis using machine learning: A case of Changsha, China

Abstract

Previous studies mainly focused on the influences of climate change on wildfires. However, other types of fires are also weather-related (especially temperature-related). The present study is aimed to analyze the influences of climate warming on fire risk. By data joining and processing, a dataset was born which includes 20,622 fire incidents and the related weather data from 2011 to 2017 in Changsha, China. Predictive models of fire frequency were established based on different regression methods of machine learning (random forest, support vector machine and polynomial). Among them, random forest regression models had the best fitting performance, and were selected to predict the fire frequency under climate warming scenarios. Under the current warming rate in Changsha, the annual fire frequency in 2067 (50 years after 2017) will increase by 0.69% to 0.89%. By rebuilding predictive models for other cities based on the proposed methods in this study, the influences of climate warming on their fire frequencies can also be analyzed.

Keywords

• Fire frequency
• Temperature
• Climate warming
• Predictive model
• Machine learning