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机器学习的应用在模拟采集区属性与数据稀缺地区的下游水质之间的关系
Miljan Kovačević1, Bahman Jabbarian Amiri2, Silva Lozančić3
1Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, Serbia.
Toxics
|December 22, 2023
概括
机器学习模型通过分析水域特征,有效地预测水质参数. 随机森林模型在评估可持续水资源管理的各种水质指标方面表现卓越.
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:
- 有效的水质管理需要了解流域物理属性与下游水质之间的联系.
- 像地质透性和土壤类型这样的捕获特征会影响水质参数.
研究的目的:
- 评估机器学习模型在预测关键水质参数方面的有效性.
- 在水质预测中确定个别输入变量的意义.
- 建立流域特征与11个入流水质量参数之间的关系.
主要方法:
- 利用了伊朗88个水站的5年 (1998-2002) 的水质数据.
- 应用机器学习方法来预测包括SAR,Na+,Mg2+,Ca2+,SO42-,Cl-,HCO3-,K+,pH,EC和TDS的参数.
- 使用皮尔森线性相关系数 (R) 和平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 随机森林 (RF) 模型在多个水质参数中表现最好.
- 确定了采水区特征和下游水质之间的显著关系.
- 提供了11个关键水质指标的预测模型.
结论:
- 机器学习,特别是射频,为预测水质参数提供了可靠的方法.
- 研究结果支持针对环境可持续性和弹性水生生态系统的有针对性的战略.
- 这项研究为平衡人类活动与保护水资源提供了实际框架.
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