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Updated: May 12, 2025

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可解释的基于人工智能的风险评估,对韩国上空的暴雨洪水
Eunmi Lee1, Young-Wook You2, Young-Hun Jung2
1Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea.
Journal of environmental management
|May 7, 2025
概括
本研究介绍了一种基于机器学习 (ML) 模型的分析层次过程 (AHP),用于评估雨水洪水风险的不确定性. 该方法有效预测经济损失,并确定关键风险因素,改进洪水风险管理策略.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 风险管理 风险管理
背景情况:
- 分析层次流程 (AHP) 是常见的暴雨洪水风险评估,但缺乏强大的不确定性评估专家判断.
- 现有的方法难以量化基于专家的洪水风险评估中固有的不确定性.
研究的目的:
- 提出和验证基于机器学习 (ML) 模型的AHP方法来评估雨水洪水风险的不确定性.
- 通过将ML与AHP集成,提高洪水风险评估的准确性和可靠性.
主要方法:
- 利用了来自韩国的 (2002-2021) 雨水洪水数据 (危险,暴露,脆弱性,能力).
- 训练 eXtreme Gradient Boosting (XGBoost) 和随机森林 (RF) 模型,使用21个变量预测经济洪水损失.
- 使用XGBoost和RF的 permutation重要性得分来估计因子权重和它们的置信区间来评估不确定性.
主要成果:
- XGBoost和RF模型准确地预测了洪水造成的经济损失,表现优于LightGBM和CatBoost.
- 确定降雨量,河流面积,人口密度和绿带面积是预测洪水损害的关键因素.
- XGBoost (RF) 突出了不透的面积分数 (河流区域) 作为一个关键的暴露成分,揭示了韩国主要城市的风险差异.
结论:
- 提出的基于ML的AHP方法对于评估洪水风险不确定性来说是实用的.
- 调查结果强调了平衡的土地开发和绿色基础设施对于有效的洪水减缓战略的重要性.
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