通过整合集群和回归方法开发一个盆地规模的总预测模型
Su Han Nam1, Siyoon Kwon2, Young Do Kim1
1Department of Civil and Environmental Engineering, Myongji University, Yongin, South Korea.
The Science of the total environment
|February 10, 2024
概括
在河流中管理总 (T-N) 对于防止缩至关重要. 这项研究使用集群和回归开发了准确的T-N预测模型,改善了Nakdong河流域的水质管理.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 数据科学数据科学数据科学
背景情况:
- 全球肥沃化是由营养物质的排放驱动的,由于高总 (T-N) 的原因,Nakdong河面临着严重的藻类繁殖挑战.
- 传统的TN监测依赖于劳动密集型采样,限制高频数据收集.
- 直接T-N传感器不可用,需要替代回归方法来准确估计.
研究的目的:
- 使用历史水质数据集,为Nakdong河流域开发准确的总 (T-N) 预测模型.
- 为了评估聚类技术的有效性与T-N估计的各种回归模型相结合.
- 为改善流域水质管理提供一个框架.
主要方法:
- 从纳克东河流域 (2018-2022) 的157个观测站收集了水质数据.
- 应用K-means集群,根据水质特征将盆地划分为四个不同的区域.
- 在每个集群中比较多重线性回归 (MLR),随机森林和XGBoost模型用于T-N预测,选择MLR作为最佳. 使用递归特征消除来进行参数选择.
主要成果:
- 将K-means分成四组为空间分析和提高回归精度提供了最合适的方法.
- 在所有集群中,MLR模型表现出合理的准确性,递归特征消除识别了高准确性TN预测的关键参数.
- 开发的基于集群的回归模型有效地预测了盆地规模的T-N水平.
结论:
- 结合K-means集群和MLR,提供了一个强大的,准确的方法来预测T-N在大型河流盆地.
- 这种方法增强了对水质的空间理解,并支持有效管理水资源的知情决策.
- 该研究强调了在全国水质监测和管理战略中广泛应用的潜力.
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