2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Eidson, Gene W

Hallstrom, Jason O

Sharp, Julia L

Taylor, Ian

White, David L

English

Clemson University: TigerPrints

A Local Correlation Score to Monitor Sensor Drift of Real-TimeEnvironmental DataIan Taylor, Dr. Julia L. Sharp, Dr. David L. White, Dr. Jason O. Hallstrom, Dr. Gene W. EidsonClemson University, Clemson, SC®IntroductionIQuality control (QC) of environmental streaming data requires multiplelevels of automated checks in order to produce quality data in a timelymanner.IThe Intelligent River R© project’s automated QC applies the LocalCorrelation Score to pairs of sensors in real time to detect sensor drift andchanges in overall sensor performance [1].IThe Local Correlation (LoCo) Score can be computed using either of twomethods: exponential window or sliding window [2]. Each methoddepends upon three parameters:I Window size (w).I The number of eigenvectors to use from the autocovariance matrixdecomposition (k).I The sliding window method relies on the number of previous windowstaken into account (m).I The exponential window method relies on the exponential decay factorof the weight of each previous window in consideration (β, 0 < β < 1).Figure: Illustration of the sliding and exponential window methods.(Adapted from similar figure in [2].)IOur goal is to determine which combination of values for theseparameters produces a sensitive correlation score to detect anomalies inpairs of sensors that may be flagged as potentially erroneous.MethodsIThe Intelligent River Project R© provides real-time monitoring, analysisand management of water resources in South Carolina through thedeployment of many sensors placed in watersheds throughout the state [1].IThe Local Correlation (LoCo) Score is a measure designed to capturethe local correlations among pairs of time-evolving time series.I The exponential window method weights each previous window basedon powers of the parameter β, while the sliding window method uses mprevious windows all of equal weight.I The LoCo score is based on the computation of the autocovariancematrix for each time series, and then comparing these matrices via theirmatrix decomposition.I A generalized notion of linear cross-correlation is produced [2].ResultsIThe LoCo score was calculated using turbidity data from second-order streams locatedin Dunn Hollow and Hembree Hollow in Eastern Tennessee from July 6, 2010 and July13, 2010. These locations are ideal because of their physical proximity, but havediffering levels of anthropogenic activity near each site.IObservations within the two data sets are typically similar, with occasional anomaliesin correlation due to extraneous factors like human interference and environmentalconditions.Figure: Results of sliding window method and exponential window method withvarying window sizes. Other parameters are held constant at k = 5, m = 2, and β = 0.5.Figure: Results of sliding window method and exponential window method withvarying numbers of eigenvectors used in computation. Other parameters are heldconstant at w = 15, m = 2, and β = 0.5.Figure: Results of the sliding window method with varying numbers of previouswindows used in computation, and of the exponential window method with varyingexponential decay constants. Other parameters are held constant at w = 15 and k = 5.(Note: the values of m and β were selected because they loosely correspond to eachother in function [1].)ConclusionsIFor both the sliding window and exponential window method:I Anomalies are detected more reliably as window size increases (i.e.,when the window size is large, anomalies are detected more quickly).However, the benefits of a larger window size (w ≥ 15) are minimal, asthe correlation scores do not differ greatly for these window sizes.I A fewer number of eigenvectors (k) used in computation seems to alsobe adequate in detecting anomalies, as well as reducing the LoCo scorecomputation time.IFor the sliding window method, the LoCo score is most sensitive to detectanomalies when a lower value of m is used. However, this may result inthe exaggeration of smaller anomalies.IFor the exponential method, the LoCo score is most sensitive to detectanomalies when a lower value of β is used. However, lower values of β donot exaggerate smaller anomalies.IFrom our study, we determine that for QC of environmental, streamingdata, the exponential LoCo score, with LoCo parameter values w = 15,k = 2, and β = 0.5, is most appropriate for identifying anomalies amongpairs of sensors.Figure: Exponential LoCo score with LoCo parameter values w = 15,k = 2, and β = 0.5References1 “Intelligent River R©: from observational to operational,” website, 2012,http://www.intelligentriver.org.2 S. Papadimitriou, J. Sun, and P. Yu, “Local correlation tracking in timeseries,” in Data Mining, 2006. ICDM ’06. Sixth International Conferenceon, dec. 2006, pp. 456-465.AcknowledgmentsThis work was supported by grants from the National Science Foundation(CNS-1126344), National Park Service - Great Smoky MountainsNational Park Grant #2097821, and the City of Aiken, SC.Mail: itaylor@clemson.edu or jsharp@clemson.edu

A Local Correlation Score to Monitor Sensor Drift of Real-Time Environmental Data

https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=1351&context=scwrc

A Local Correlation Score to Monitor Sensor Drift of Real-Time Environmental Data

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