Flow-directed PCA for monitoring networks.
Flow-directed PCA for monitoring networks.
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DOI：
10.1002/env.2434
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发表时间：
2017-03
期刊：
影响因子：
1.7
通讯作者：
Douglass J
中科院分区：
文献类型：
--
作者：
Gallacher K;Miller C;Scott EM;Willows R;Pope L;Douglass J
在监测网络上记录的测量结果往往具有空间和时间相关性，导致所提供的信息存在冗余。特别是对于河流水质监测，流动连接的站点可能会提供类似的信息。本文提出了一种新的主成分分析方法，以研究时空流连接网络数据的降维，以识别共同的时空模式。该方法说明了在英格兰的特伦特流域的总氮氧化物的每月观测。共同的模式被发现时，隐藏的河网结构和时间相关性不占。这些模式为今后设计抽样战略提供了宝贵的信息。
Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow‐connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow‐connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies.