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一种绝对的机器学习方法来预测浅海沿海地下水的区域
Patrick Durney1,2,3, Matt Dumont2, Christo Rautenbach3,4
1Lincoln Agritech, Canterbury, New Zealand.
Ground water
|September 12, 2025
概括
沿海低地面临着海平面上升. 这项研究使用了一种新的分类模型来估计地下水深度,改善了不确定性量化,以改善沿海管理.
科学领域:
- 环境科学环境科学
- 地质科学是地球科学.
- 水资源管理水资源的管理.
背景情况:
- 沿海低地越来越容易受到海平面上升和地下水变化的影响.
- 准确的地下水深度估计对于沿海适应规划至关重要.
- 传统的回归方法经常与数据不平衡和不确定性量化作斗争.
研究的目的:
- 为地下水深度估计引入一种新的分类建模框架.
- 将地下水深度估计重新定义为分类问题,并纳入不确定性量化.
- 为沿海管理提供基于证据的决策提供一个强大的工具.
主要方法:
- 使用随机森林算法开发了一个100米分辨率的全国范围的集合模型.
- 在新西兰的水深数据库中训练模型,其中包含37个主要组件衍生的预测变量.
- 将地下水发生情况分类为多个深度值 (0.9-2.0m),以通过I型和II型错误量化预测不确定性.
主要成果:
- 整体模型实现了强的性能,ROC-AUC值在0.823到0.962.9之间.
- 模型的准确性证明,随着地下水深度的增加,模型的准确性得到了改善.
- 分类框架有效地解决了数据不平衡问题,并加强了不确定性量化.
结论:
- 与传统的回归方法相比,分类建模方法提供了更好的不确定性量化方法.
- 概率预测为沿海管理的利益相关者提供了可定制的风险门.
- 该框架将先进的建模与实际的适应海平面上升规划相结合.
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