为了捍卫指标:指标足够编码典型的人类偏好关于水文模型性能
Martin Gauch1,2, Frederik Kratzert3, Oren Gilon4
1Institute for Machine Learning Johannes Kepler University Linz Austria.
概括
水文学家 水文学家
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 环境建模环境建模
背景情况:
- 准确的降雨-流水模型对于水文科学和实践至关重要.
- 许多评估指标存在,但水文学家仍然普遍使用视觉判断.
- 视觉判断和定量指标之间的一致性尚未得到充分理解.
研究的目的:
- 调查专家视觉判断和定量指标在评估降雨-流水模型中的一致性.
- 确定专家意见是否与已建立的定量绩效指标保持一致.
- 评估定量指标在水文模型评估中反映人类偏好方面的可靠性.
主要方法:
- 聘请了622名水文专家,评估了来自13个不同的模型的14000多对水文图.
- 专家提供了对模型性能的视觉判断.
- 量化指标与专家意见进行了比较.
主要成果:
- 专家意见通常与定量指标一致,显示机器学习模型比传统水文模型更受青.
- 在专家意见中观察到显著的不一致.
- 量化指标可以预测专家在存在共识的情况下达成一致,这表明它们捕捉了人类的偏好.
结论:
- 定量指标足以编码人类偏好用于水文模型评估.
- 虽然改进是可能的,但水文界应该增加对基准测试的定量指标的依赖.
- 建议加强对量化指标的信任,以加强对基准测试的努力.
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