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彻底的重要性:数据分析选择如何影响污染物和预测因素之间的感知关系
Laura Lotteraner1, Torsten Möller2, Thilo Hofmann3
1Faculty of Computer Science, University of Vienna, Währinger Straße 29, 1090 Vienna, Austria; Department of Environmental Geosciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria; Doctoral School Computer Science, University of Vienna, Währinger Straße 29, 1090 Vienna, Austria; Research Platform The Challenge of Urban Futures, University of Vienna, Rooseveltplatz 2, 1090 Vienna, Austria.
数据准备对河流中制药污染的分析产生重大影响. 适当处理低于检测极限的值和数据聚合对于准确的社会经济驱动洞察至关重要.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 生态毒理学 生态毒理学
背景情况:
- 了解表面水污染的社会经济驱动因素对于水生生态系统和饮用水保护至关重要.
- 数据驱动的方法越来越多地用于环境污染分析.
- 环境数据集通常包含诸如低于检测极限的值和极端值等挑战.
研究的目的:
- 调查数据准备方法对河流中药物污染分析的影响.
- 展示数据特征如何影响统计建模结果.
- 为社会经济驱动因素和污染提供强有力的分析提供建议.
主要方法:
- 利用全球制药污染河流污染数据集.
- 应用各种数据准备技术,包括处理检测,聚合和规范化极限以下的值.
- 执行了线性回归模型和组比较测试.
主要成果:
- 数据准备方法的选择显著改变了线性回归和组对比测试的结果.
- 处理低于检测极限的值尤其具有影响.
- 不同的数据准备组合产生了对污染社会经济驱动因素的不同见解.
结论:
- 数据准备是环境科学中的统计建模的关键组成部分.
- 仔细记录和讨论数据准备步骤至关重要.
- 数据准备的标准化程序可以提高从污染数据中得出的结论的可靠性.
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