Version 1
: Received: 3 March 2024 / Approved: 3 March 2024 / Online: 4 March 2024 (09:57:46 CET)
How to cite:
Hai, Q.; Han, X.; Vandansambuu, B.; Bao, Y.; Gantumur, B.; Bayarsaikhan, S.; Chantsal, N.; Sun, H. Predicting the Occurrence of Forest Fire in the Central-South Region of China. Preprints2024, 2024030089. https://doi.org/10.20944/preprints202403.0089.v1
Hai, Q.; Han, X.; Vandansambuu, B.; Bao, Y.; Gantumur, B.; Bayarsaikhan, S.; Chantsal, N.; Sun, H. Predicting the Occurrence of Forest Fire in the Central-South Region of China. Preprints 2024, 2024030089. https://doi.org/10.20944/preprints202403.0089.v1
Hai, Q.; Han, X.; Vandansambuu, B.; Bao, Y.; Gantumur, B.; Bayarsaikhan, S.; Chantsal, N.; Sun, H. Predicting the Occurrence of Forest Fire in the Central-South Region of China. Preprints2024, 2024030089. https://doi.org/10.20944/preprints202403.0089.v1
APA Style
Hai, Q., Han, X., Vandansambuu, B., Bao, Y., Gantumur, B., Bayarsaikhan, S., Chantsal, N., & Sun, H. (2024). Predicting the Occurrence of Forest Fire in the Central-South Region of China. Preprints. https://doi.org/10.20944/preprints202403.0089.v1
Chicago/Turabian Style
Hai, Q., Narantsetseg Chantsal and Hailian Sun. 2024 "Predicting the Occurrence of Forest Fire in the Central-South Region of China" Preprints. https://doi.org/10.20944/preprints202403.0089.v1
Abstract
Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.
Keywords
wildfire risk assessment; Central-South China; GIS application; predictive modeling; fire occurrence analytics; seasonal fire patterns; spatial clustering analysis
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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