Automatic water mixing event identification in the Koljö fjord observatory data

Automatic water mixing event identification in the Koljö fjord observatory data 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Data Science and Analytics Springer Journals

Automatic water mixing event identification in the Koljö fjord observatory data

Automatic water mixing event identification in the Koljö fjord observatory data

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 the water mixing events with a F1-measure of 0.885, a precision of 0.931—that is 93.1% of all events have been correctly detected—and a recall of 0.843–84.3% of events that should have been found actually also have been. Using the proposed method, the oceanographers can be informed automatically about the status of the fjord without manual interaction or physical presence at the experiment site. Keywords Multivariate time series analysis · Koljö fjord observatory · Water mixing event detection · Clustering · DBSCAN 1 Introduction Many important events can be observed using underwa- ter sensor systems. Algal blooms,...
Loading next page...
 
/lp/springer_journal/automatic-water-mixing-event-identification-in-the-kolj-fjord-FPFyQgYDIe
Publisher
Springer International Publishing
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Database Management; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics; Business Information Systems
ISSN
2364-415X
eISSN
2364-4168
D.O.I.
10.1007/s41060-018-0132-z
Publisher site
See Article on Publisher Site

Abstract

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

Journal

International Journal of Data Science and AnalyticsSpringer Journals

Published: Jun 6, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off