对于地质污染地下水的机器学习模型的可转移性
Hailong Cao1, Xianjun Xie2,3, Ziyi Xiao2,3
1College of Resources and Environment, Yangtze University, Wuhan 430100, China.
Environmental science & technology
|May 8, 2024
概括
在使用水化数据时,将地下水污染的机器学习模型转移是可行的. 添加本地数据显著提高了模型的准确性,强调了预测类型和数据信息对于成功的模型传输的重要性.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 模型对于识别地质污染地下水是有效的.
- 在数据稀缺的地区开发ML模型具有挑战性,因为需要广泛的培训数据集.
- 模型可转移性为在数据有限的地区应用ML提供了潜在的解决方案.
研究的目的:
- 调查高化物地下水ML模型在山西裂系统内的不同盆地之间的可转移性.
- 评估六个因素的影响:建模方法,预测器类型,数据大小,样本/预测器比率 (SPR),预测器范围,以及对模型可转移性的数据.
- 确定增强或阻碍ML模型用于地下水污染评估的成功转移的关键因素.
主要方法:
- 使用水化和表面参数作为预测指标,探索ML模型的可转移性.
- 评估数据信息化 (将目标地区的数据添加到培训组) 对可转移性的影响.
- 统计分析 (逐步回归) 以确定影响可转移性的重要因素.
- 应用t分布式静态邻居嵌入 (t-SNE) 算法用于数据可视化和跨盆地的比较.
主要成果:
- 高化物地下水模型的成功可转移性仅在预测指标基于水化参数而不是表面参数时才能实现.
- 通过将来自具有挑战性的地区的样本纳入数据信息,显著提高了模型的可转移性.
- 逐步回归证实了水化预测因素和数据信息作为显著的积极因素,而数据大小,SPR和预测范围没有显著的影响.
- 先进的ML模型 (随机森林,人工神经网络) 在可转移性方面并不总是超过后勤回归.
- t-SNE分析强调了预测类型在实现有效数据表示和跨盆地模型转移方面发挥的关键作用.
结论:
- 水化学参数对于机器学习模型在地下水污染研究中的成功可转移性至关重要.
- 数据信息化是改善数据有限或具有挑战性的水文地质环境中转移模型性能的重要策略.
- 预测器类型的选择对于模型可转移性来说比使用的机器学习算法的复杂性更为关键.
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