绘制知识导向机器学习的空间可转移性:用于预测排水流量分数的应用
Raphael Schneider1, Saskia Noorduijn1, Elisa Bjerre2
1Geological Survey of Denmark and Greenland (GEUS), Department of Hydrology, Copenhagen, Denmark.
The Science of the total environment
|January 10, 2025
概括
这项研究开发了一种机器学习 (ML) 的元模型,以估计丹麦的排水分数. 该方法成功地为71%的农田绘制了排水分数,并使用适用性领域概念评估了空间可转移性.
科学领域:
- 水文科学 水文科学
- 环境建模环境建模
- 地理空间分析是什么
背景情况:
- 机器学习 (ML) 方法在水文学中越来越多地用于大规模预测.
- 机器学习模型的空间可转移性至关重要,但往往未被充分探索.
- 人工排水对水文过程和营养物质运输产生重大影响.
研究的目的:
- 使用ML开发一个大规模排水分数估计10米分辨率的元模型.
- 评估ML元模型在丹麦的空间可转移性.
- 使用适用性区域 (AOA) 识别模型预测可靠的领域.
主要方法:
- 使用了渐变增强决策树 (GBDT) ML算法.
- 一个元模型将地形,土地利用和地质共变量与来自45个基于物理模型的模拟排水分数结合起来.
- 适用性区域 (AOA) 用于绘制模型的可转移性和可靠性.
主要成果:
- ML元模型成功地估计了丹麦农田在全国范围内的排水分数.
- 丹麦71%的农业用地被发现属于该模型的适用范围.
- 该研究提供了一种方法来升级本地规模的水文模型,并评估空间可转移性.
结论:
- 开发的ML元模型为全国范围的排水分数绘制提供了一种可行的方法.
- 通过AOA评估空间可转移性对于水文学中可靠的ML应用至关重要.
- 这些发现支持了关于农业排水的土地和水资源管理决策.
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