探索斯里兰卡凯兰尼河的空间和季节性水质变化:一种潜在的变量方法
Nalintha Wijayaweera1,2, Luminda Niroshana Gunawardhana3,4, So Kazama5
1Department of Civil Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka. wijayaweerapn.23@uom.lk.
Environmental monitoring and assessment
|October 17, 2024
概括
机器学习有效地分析了斯里兰卡复杂的河流水质数据. 潜变量分析确定了污染源和季节性变化,有助于水资源管理策略.
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:
- 全球水质恶化是一个重大挑战,特别是在发展中国家.
- 河流污染监测产生了大量的数据集,通常压倒了传统的分析方法.
- 斯里兰卡面临着不断升级的河流污染,需要先进的数据处理技术.
研究的目的:
- 通过使用潜变量 (LV) 和无监督机器学习来研究空间和季节性地表水质变化.
- 确定斯里兰卡凯兰尼河的关键水质参数和污染源.
- 评估LV方法在管理复杂的水质数据中的有效性.
主要方法:
- 在17个地点 (2016-2020年) 用了17个参数的月度水质数据.
- 应用了Pearson的相关性,因子分析 (FA) 来生成LV,以及层次的集群/自我组织映射.
- 分析了空间和季节性变化,并将水质与水文气象数据相关联.
主要成果:
- 因子分析确定了五个LV,解释了77%的总差异,揭示了多种污染类型.
- 聚类方法对站点进行了类似的分组,突出显示工业区和河口附近的高差异.
- 工业废水和污水显示出显著的季节性波动,在下基拉尼河流域的变化增加.
结论:
- 拟议的潜变量方法有效处理复杂的多参数水质数据.
- LV分析成功地确定了污染源 (工业废水,污水) 和它们的季节性模式.
- 这种方法可以帮助当局应对斯里兰卡和类似地区的河流污染挑战.
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