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Updated: Sep 13, 2025

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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
Published on: November 13, 2017
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一个基于机器学习的自动化框架,用于用传感器数据预测地下水质量
Jaeuk Youn1, Do Hwan Jeong2, MoonSu Kim2
1Department of Environmental & Energy Engineering, Yonsei University, Wonju, 26493, Republic of Korea.
Journal of environmental management
|July 27, 2025
概括
一个自动化框架使用传感器数据和机器学习准确预测地下水中的氨 (NH3-N). 这种方法增强了地下水质量监测和污染检测.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 地下水质量监测对于有效的地下水管理至关重要.
- 实时和准确的测量技术对于及时干预至关重要.
- 现有的方法可能缺乏持续监控所需的精度和速度.
研究的目的:
- 开发一个用于预测地下水中的氨 (NH3-N) 的自动化框架.
- 利用多参数传感器数据和机器学习来提高准确性.
- 提高地下水质量监测的效率和可靠性.
主要方法:
- 从尸体埋葬地点收集传感器数据,随后进行严格的质量控制和实验室校准.
- 应用自动机器学习 (AutoML) 用关键功能优化NH3-N预测模型.
- 在各种水文地质数据集上验证了框架的预测性能.
主要成果:
- 优化模型显著提高了预测准确性:R2从0.76增加到0.90.
- 错误指标大幅下降:RMSE从0.84降至0.38,MAE从0.57降至0.23.
- 外部验证证实了不同地区的强表现 (R2 = 0.89-0.98).
结论:
- 拟议的框架有效地将校准的传感器数据与可靠的地下水监测的自动化模型选择相结合.
- 这种方法提供了一个可扩展的解决方案,用于在敏感环境中早期检测污染.
- 先进的分析和自动校准增强了污染警报,支持积极的地下水管理.
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