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使用机器学习预测开发水域的溪流盐化情况的理解.
Jared D Smith1, Lauren E Koenig1, Margaux J Sleckman2
1Water Mission Area, Integrated Modeling and Prediction Division, U.S. Geological Survey, Reston, Virginia 20192, United States.
Environmental science & technology
|October 11, 2024
概括
机器学习模型估计了每日河流的盐度,这对于生态评估至关重要. 这些模型识别了污染源,如冰,帮助流域管理和保护水生生态系统.
科学领域:
- 环境科学环境科学
- 水文学的水文学
- 机器学习 机器学习
背景情况:
- 河流盐化是一个日益严重的全球问题,影响水生生态系统.
- 现有的模型往往缺乏有效的生态暴露评估的空间和时间分辨率.
- 准确的盐度估计对于管理流域的人为影响至关重要.
研究的目的:
- 开发和比较机器学习模型,以估计在未监控位置的每日流特定导电性.
- 使用可解释的人工智能识别河流盐化的关键驱动因素.
- 评估模型在识别因脱冰器应用而受损的河流的有用性,以进行有针对性的管理.
主要方法:
- 利用高频监测和离散的水质样本进行模型培训.
- 将空间和时间无意识的随机森林模型与空间和时间意识的反复图形卷积神经网络 (RGCN) 模型进行比较.
- 应用可解释的AI技术来解释模型预测和盐化驱动因素.
主要成果:
- RGCN模型的预测性能 (KGE:0.67和0.64) 与随机森林模型的预测性能相当.
- 模型准确地捕获了季节性盐度模式,包括冬季的"第一次冲洗"的脱冰器.
- 高盐度预测与脱冰应用和城市化指标的相关性很好.
结论:
- 机器学习模型可以可靠地估计流域的每日盐度.
- 这些模型有效地识别人为盐化源,特别是冬季的冰川流水.
- 该方法可应用于其他流域,支持有针对性的最佳管理实践和风险评估.
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