论文标题
关于使用降低降低或信号分离方法用于氮河污染源识别
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification
论文作者
论文摘要
识别当前和预期的未来污染源对河流的污染源对于合理的环境管理至关重要。为此，提出了许多方法，可以在基于物理的模型，稳定的同位素分析和混合方法，质量平衡方法，时间序列分析，土地覆盖分析和空间统计数据下聚类。另一个极为常见的方法是主成分分析及其修改，例如绝对主成分得分。它们已应用于氮气进入河流的源识别问题。该手稿正在检查PCA是否真的可以成为考虑其理论背景和假设的氮污染源的强大方法。此外，还将考虑稍微相似的技术，独立的组件分析和因子分析。
Identification of the current and expected future pollution sources to rivers is crucial for sound environmental management. For this purpose numerous approaches were proposed that can be clustered under physical based models, stable isotope analysis and mixing methods, mass balance methods, time series analysis, land cover analysis, and spatial statistics. Another extremely common method is Principal Component Analysis, as well as its modifications, such as Absolute Principal Component Score. they have been applied to the source identification problems for nitrogen entry to rivers. This manuscript is checking whether PCA can really be a powerful method to uncover nitrogen pollution sources considering its theoretical background and assumptions. Moreover, slightly similar techniques, Independent Component Analysis and Factor Analysis will also be considered.
