机器学习模型用于预测农业流域地表水中的营养度
Ahmed Elsayed1, Sarah Rixon2, Jana Levison2
1School of Engineering, Morwick G360 Groundwater Research Institute, University of Guelph, Guelph, Ontario, Canada; Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, Giza, Egypt.
Journal of environmental management
|November 19, 2024
概括
机器学习模型使用气候,水文和现场数据准确预测地表水的营养度. 这有助于管理农业流域的水质.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 农业水文学 农业水文
背景情况:
- 农业和城市流域的过量营养物质降低了地表水质.
- 机器学习 (ML) 为了解营养动态提供了一种强大的方法.
- 缺乏对ML模型进行系统的研究,以预测农业环境中的地表水营养素.
研究的目的:
- 评估各种分类和回归ML模型来预测地表水中的营养度.
- 确定最佳的ML算法,用于评估农业流域的地表水质量.
- 利用气候,水文和现场数据作为营养预测的输入变量.
主要方法:
- 应用多重分类 (例如决策树) 和回归 (例如回归树) ML模型.
- 利用加拿大安大略省南部上帕克希尔流域的数据集.
- 输入变量包括气候,水文和田地特征;目标变量是营养度 (酸盐,形式).
主要成果:
- 结合袋式树和后勤回归,实现了高分类准确性 (CA ≥ 0.72).
- 指数高斯过程回归证明了回归任务的优异性能 (R2 ≥ 0.93).
- 最佳的ML模型显示了对目标营养变量的高预测准确度.
结论:
- ML模型是预测和量化地表水营养度的有效工具.
- 这些模型可以补充农业流域的现场监测数据.
- 有效的营养预测有助于保持高表面水质资源.
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