1. Flow-directed PCA for monitoring networks
- Author
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R. Willows, K. Gallacher, J. Douglass, E. M. Scott, L. Pope, and Claire Miller
- Subjects
Statistics and Probability ,010504 meteorology & atmospheric sciences ,Computer science ,Ecological Modeling ,Flow (psychology) ,Network data ,Sampling (statistics) ,Temporal correlation ,computer.software_genre ,01 natural sciences ,River water ,6. Clean water ,010104 statistics & probability ,Principal component analysis ,Data mining ,Catchment area ,0101 mathematics ,computer ,0105 earth and related environmental sciences ,Curse of dimensionality - Abstract
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.
- Published
- 2016
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