可解释机器学习用于解释和预测长白山区的崩危险
Xiangyang He1, Qiuling Lang1, Jiquan Zhang2
1School of Jilin Emergency Management, Changchun Institute of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究使用机器学习来评估长白山的崩塌危险,发现优化的随机森林模型最能预测风险. 与道路的距离被确定为崩管理的关键因素.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 长白山区的崩塌危险是由于地质,气象和人为因素之间的复杂相互作用造成的.
- 有效的危险评估需要先进的分析技术来处理复杂的,非线性数据结构.
研究的目的:
- 通过评估复杂相互作用的机器学习模型来分析崩危险.
- 确定关键的风险因素,并提高危险评估模型的解释性.
主要方法:
- 利用651个崩事件的数据集来评估支持向量机 (SVM),随机森林 (RF),极端梯度增强 (XGBoost) 和光梯度增强机 (LightGBM).
- 采用差异通胀因子来优化崩风险因子选择.
- 集成的沙普利添加式解释 (SHAP) 与可解释的人工智能,以提高模型的透明度.
主要成果:
- 与SVM,XGBoost和LightGBM相比,优化的随机森林模型表现出更高的性能.
- 沙普利补充解释分析发现"距离道路的距离"是影响塌危险的重要因素.
- 该研究为评估的机器学习模型提供了统计验证的性能指标.
结论:
- 机器学习,特别是优化的随机森林模型,为评估复杂的崩危险提供了强大的方法.
- 像SHAP这样的可解释的人工智能方法对于理解和管理崩风险至关重要.
- 调查结果强调了在崩管理策略中考虑基础设施附近的重要性.
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