沿海水质模型使用E. coli,气象参数和机器学习算法
Athanasios Tselemponis1, Christos Stefanis1, Elpida Giorgi1
1Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece.
概括
机器学习模型准确地对马其顿东部和特拉西亚的E. coli (大肠杆菌) 的沿海水质进行了分类. 实现了高精度,超过99%,表明了极好的水条件.
科学领域:
- 环境科学 环境科学
- 机器学习 机器学习
- 水质管理水质管理
背景情况:
- 沿海水质监测对公共卫生和生态系统完整性至关重要.
- 根据2006/7/EC指令,人们必须定期评估泳池水的质量.
- 大肠杆菌 (E. coli) 是海洋环境中便污染的关键指标.
研究的目的:
- 实施和评估机器学习模型,以根据大肠杆菌度预测沿海水分类.
- 评估气象变量对沿海水域大肠杆菌水平的影响.
- 根据欧盟标准对东马其顿和特拉斯 (EMT) 沿海地区的水质进行分类.
主要方法:
- 在2009年至2021年 (五月至九月) 期间,从EMT的六个采样站收集了1039个水样.
- 使用ISO 9308-1标准对大肠杆菌进行分析.
- 从附近的站点获取气象数据.
- 应用机器学习分类器包括决策森林,决策林和增强决策树.
主要成果:
- 绝大多数样本被归类为1类 (优秀).
- 决策森林,决策林和提升决策树分类器的准确度得分超过99%.
- 与其他研究的比较表明,各种机器学习算法 (决策树,人工神经网络,贝叶斯信念网络) 对水质预测产生了令人满意的结果.
结论:
- 机器学习模型有效预测沿海水质和大肠杆菌污染动态.
- 气象参数可以整合到水质分类模型中.
- EMT的沿海水域表现出极好的质量,使用先进的计算方法具有很高的可预测性.
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