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来自多个水体的最佳数据聚合,以改进机器学习预测蓝藻细菌开花的预测

Jayun Kim¹, Joonhong Park², Hyun Je Oh³

¹Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea; Division of Environmental Health Sciences, The Ohio State University, OH 43210, USA.

Journal of contaminant hydrology

|November 6, 2025

概括

从多个水体汇集数据可以改善蓝藻细菌繁殖 (CB) 预测模型. 通过类似的CB模式对网站进行聚类可以提高模型的性能,为水资源管理和公共卫生保护提供一种实际的方法.

科学领域:

环境科学环境科学
水资源管理水资源管理
机器学习应用机器学习应用

背景情况:

对于水资源保护和公共卫生而言,准确预警蓝菌繁殖 (CBs) 非常重要.
来自个别监测站点的有限数据阻碍了机器学习 (ML) 模型的泛化.
从多个水体汇集数据可以增加样本大小,但可能引入异质性,降低模型可转移性.

研究的目的:

通过集群分析评估一个务实的数据聚合策略,用于预测蓝藻细菌繁殖.
量化来自类似水体的数据聚合对ML模型性能的影响.
为选择多站点数据集提供准则,以改进基于ML的CB预测.

主要方法:

层次分类被用来将45个韩国湖泊和河流分成八个不同的集群,这些集群是基于网站级CB模式的.
采用随机森林 (RF) 和神经网络 (NN) 模型,利用聚合和单个站点数据预测CB.
预测性能通过比较训练在来自类似集群的聚合数据的模型与不相似的集群和单个站点的模型来评估.

主要成果:

将多个站点的数据汇集在一起,通常可以提高RF (大约. 14%的收益) 和NN (大约. 17%的收益) 的模型.
统计学上相似的集群的数据汇集时,实现了最大的绩效增长,超过了跨不同站点的汇集.
模型性能随着数据集大小的增加而增加,在400-500个样本左右停滞不前,从不到十个类似站点获得最佳收益.
水温和营养变量是关键预测因素,而降雨效应因时间和水体类型而异.

结论:

基于CB模式的水体聚类是务实数据聚合的有效策略,以提高ML模型的概括性.
从类似的地点汇集数据,即使跨越很远的距离,也显著提高了CB预测的准确性.
该研究为选择最佳的多站点数据集提供了有价值的指导方针,以改善基于ML的水质监测和预警系统.

关键词:

藻类的开花藻类的开花控制的河流是大控制的河流.预测预测预测预测多个站点的数据聚合.预测的准确性预测的准确性

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