应用分类机器学习算法来描述粘土平原农业流域中的营养物质运输
Ahmed Elsayed1, Sarah Rixon2, Jana Levison2
1School of Engineering, Morwick G360 Groundwater Research Institute, University of Guelph, 50 Stone Road East, Guelph, Ontario, N1G 2W1, Canada; Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, 1 Gamaa Street, Giza, 12613, Egypt.
Journal of environmental management
|September 7, 2023
概括
机器学习模型有效地对农业表面水中的营养度进行分类. 集体袋式树在预测酸盐水平方面表现出色,而加权的KNN和集体子空间区分算法准确地分类了度.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 水体中的营养过多会降低水质.
- 分类营养度对于水资源管理至关重要.
- 机器学习 (ML) 提供了有效的方法来分析农田的营养损失.
研究的目的:
- 评估24ML分类算法对农业表面水中的营养度进行分类的性能.
- 确定最佳的ML算法来预测酸盐和含量.
- 利用广泛的输入变量,包括地质化学,物理,气候和现场条件.
主要方法:
- 在上帕克希尔流域的数据上实施了24ML分类算法.
- 使用地质化学和物理水参数,气候和现场条件作为输入变量.
- 使用四个指标评估算法性能,包括分类准确性.
主要成果:
- 集体袋装树木实现了90.9%的准确性酸盐分类.
- 在加权的KNN中,总分类的准确率为87%.
- 集成子空间分辨器对可溶反应和总溶解分别实现了79.2%和77.9%的准确性.
结论:
- ML算法是对农业表面水中的营养度进行分类的有效工具.
- 特定的ML算法在预测不同类型的营养素方面表现出很高的准确性.
- 这些发现支持为农业流域制定有针对性的营养管理策略.
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