This study addresses the task of automatically identifying water mixing events in the multivariate time series of salinity, temperature and dissolved oxygen provided by the Koljö fjord observatory. The observatory is used to test new underwater sensory technology and to monitor water quality with respect to hypoxia and oxygenation in the fjord and has been collecting data since April 2011. The fjord water properties change, manifesting as peaks or drops of dissolved oxygen, salinity and temperature, when affected by inﬂows of new water originating from the open sea or by rivers connected to the fjord system. An acute state of oxygen depletion can harm wildlife and the ecosystem permanently. The major challenge for the analysis is that the water property changes are marked by highly varying peak strength and correlation between the signals. The proposed data- driven analysis method extends existing univariate outlier detection approaches, based on clustering techniques, to identify the water mixing events. It incorporates three major steps: 1. smoothing of the input data, to counter noise, 2. individual outlier detection within the separate variables, 3. clustering of the results using the DBSCAN clustering algorithm to determine the anomalous events. The proposed approach is able to detect
International Journal of Data Science and Analytics – Springer Journals
Published: Jun 6, 2018
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