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一个随机的森林方法来改善对支流营养物质负载的估计
1Vermont Department of Environmental Conservation, 1 National Life Drive, Montpelier, VT 05 USA.
Water research
|November 20, 2023
概括
一个新的随机森林模型准确地估计了淡水系统中的营养负载,超过了像WRTDS这样的传统方法. 这种方法为管理水质和实现环境目标提供了更好的洞察力.
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:
- 从水质样本和溪流排放中精确估计构成负载,对于淡水资源管理至关重要.
- 营养物质负载是政府目标和了解水生生态系统反应的基础.
- 像WRTDS这样的现有模型被广泛使用,但在捕捉复杂的放电度动态方面可能存在局限性.
研究的目的:
- 开发和评估一种新的随机森林模型,用于估计关键营养素 (总,溶解,总) 和的度和负载.
- 将新的随机森林模型的性能与已建立的时间,排放和季节权重回归 (WRTDS) 模型及其卡尔曼波器扩展进行基准测试.
- 评估随机森林模型可视化功能的实用性,以获得过程洞察力.
主要方法:
- 开发一种使用随机森林的新负载估计模型.
- 该模型应用于1992年至2021年跨越香湖17条支流的数据集.
- 包括预测因素,如排放变化率和不同时间窗口的先前排放.
- 与基础WRTDS和卡尔曼过的WRTDS模型进行基准测试.
主要成果:
- 随机森林模型在大多数情况下,与基础WRTDS和卡尔曼过的WRTDS相比,表现优越.
- 该模型的有效性归因于它能够结合动态放电变量及其灵活的预测器-响应关系建模的能力.
- 随机森林模型提供了有价值的可视化工具,提供了重要的过程见解.
结论:
- 开发的随机森林模型为营养和成分负载估计提供了有希望的进步.
- 这种新方法很容易适应现有数据集,并可用于各种应用程序.
- 虽然WRTDS仍然很有价值,但随机森林模型为水质管理提供了更高的准确性和洞察力.
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