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可解释的机器学习用于预测和评估复杂的干旱影响

Beichen Zhang¹, Fatima K Abu Salem², Michael J Hayes³

¹School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.

The Science of the total environment

|July 17, 2023

概括

这项研究引入了一种可解释的机器学习方法,以准确预测干旱影响. 通过使用XGBoost和SHAP,它提高了对干旱预测的信任,有助于应对灾害.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
灾害管理灾害管理

背景情况:

干旱对社会,经济和环境造成重大损害.
机器学习 (ML) 模型是强大的预测工具,但往往缺乏透明度,妨碍对灾害评估等关键应用程序的信任.
机器学习中的可解释性对于利益相关者来说至关重要,使他们能够理解和信任模型的预测,特别是在高风险的场景中.

研究的目的:

开发一种可解释的ML管道,用于预测美国多维干旱影响.
通过可解释性,提高干旱影响评估中ML模型的可靠性.
解释干旱指标与它们在区域范围内的影响之间的关系.

主要方法:

使用了一个可解释的ML管道,集成XGBoost模型和SHAP (SHapley添加式扩展) 值.
从美国干旱影响报告员的干旱影响综合数据库中训练模型.
使用F2得分评估模型性能,与基线模型进行比较.

主要成果:

XGBoost模型的表现明显优于基线模型,平均F2得分为0.883在全国和0.942在州级.
SHAP分析确定了标准降水指数 (SPI) 和标准温度指数 (STI) 作为干旱影响的关键预测指标.
发现了可解释的关系:负的SPI值与复杂的干旱影响有积极的关联,提高了模型的可靠性.

结论:

可解释的ML,特别是使用XGBoost和SHAP,提供了一种可靠的方法来准确预测复杂的干旱影响.
该研究表明SPI和STI在预测干旱影响方面的重要性,其影响因地点和影响类型而异.
这种方法提高了利益相关者的信任,并为区域干旱管理和应对战略提供了可操作的见解.

关键词:

干旱干旱干旱干旱可解释的人工智能影响评估影响评估影响评估机器学习机器学习

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