机器学习使用全年日常野火预测和关键因素分析:韩国甘州的案例研究
Chanjung Lee1, Eun Hyoung Choi1, Youngju Han1
1Department of Agriculture, Forestry and Bioresources, Seoul National University, Seoul, 08826, Republic of Korea.
Scientific reports
|August 14, 2025
概括
机器学习模型现在可以预测韩国甘州一年中每天发生的野火. 关键因素包括天气,森林状况和人类活动,改善野火预防策略.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 林业林业 林业 林业 林业
背景情况:
- 由于气候变化和人类的影响,全球范围内的野火正在增加.
- 现有的研究往往侧重于高风险领域,而不是具体的每日预测.
- 有效的每日野火预测对于及时响应至关重要.
研究的目的:
- 使用机器学习开发全年日常野火预测模型.
- 确定影响韩国韩国刚州野火发生的关键因素.
- 为了比较不同的机器学习算法用于野火预测的性能.
主要方法:
- 综合气象,森林相关和社会经济数据.
- 采用了机器学习算法:物流回归,XGBoost,随机森林和额外树.
- 使用SHAP (夏普利添加式扩展) 来实现模型的可解释性.
主要成果:
- 额外树模型实现了最高的曲线下面积 (AUC) 0.839.
- 随机森林模型显示最好的召回率为0.828.
- SHAP分析确定了相对湿度,降水和温度作为关键的气象预测因素,以及森林和社会经济变量.
结论:
- 机器学习为日常野火预测提供了一种有效的方法.
- 整合不同的数据源 (气候,环境,人类活动) 可以提高预测的准确性.
- 这种模型可以推进实际的野火预防和应对策略,以精细的时间和空间分辨率.
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