Information theoretic perspective on coastal water-quality monitoring and management near an offshore industrial park

Ta-Kang Liu, Jin Li Yu, Chung-Ling Chen, Ping Sheng Wei

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

A semi-continuous water-quality monitoring system was installed in Yunlin Offshore Industrial Park (YOIP), the largest industrial park in Taiwan, in 2007 to provide real-time water-quality information such as pH, water depth, dissolved oxygen, temperature, turbidity, conductivity, and chlorophyll. To interpret the large quantities of high-frequency data generated by this system, information theory was applied for data analysis and extraction of useful information for further coastal water-quality management. Information theory is a branch of applied mathematics that involves the quantification of information. Shannon entropy is a key measure of information that was calculated in this study to reveal the inherent uncertainty of water-quality information. The applicability of Shannon entropy for signaling possible coastal pollution events in the YOIP was explored and results showed that it provides new insight into the inherent uncertainty or randomness of the original data. Specially, when Shannon entropy was high, multiple instable readings were observed for turbidity and salinity. This indicates that Shannon entropy may be a useful new tool for exploratory data analysis. It can be used as a supplementary indicator along with the original environmental data to signify some episodes of water-quality degradation.

Original languageEnglish
Pages (from-to)4725-4735
Number of pages11
JournalEnvironmental Monitoring and Assessment
Volume184
Issue number8
DOIs
Publication statusPublished - 2012 Aug 1

Fingerprint

Water quality
coastal water
water quality
Entropy
entropy
Monitoring
monitoring
Information theory
Turbidity
turbidity
Quality management
Chlorophyll
Dissolved oxygen
mathematics
Pollution
monitoring system
dissolved oxygen
water depth
chlorophyll
information system

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Pollution
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "A semi-continuous water-quality monitoring system was installed in Yunlin Offshore Industrial Park (YOIP), the largest industrial park in Taiwan, in 2007 to provide real-time water-quality information such as pH, water depth, dissolved oxygen, temperature, turbidity, conductivity, and chlorophyll. To interpret the large quantities of high-frequency data generated by this system, information theory was applied for data analysis and extraction of useful information for further coastal water-quality management. Information theory is a branch of applied mathematics that involves the quantification of information. Shannon entropy is a key measure of information that was calculated in this study to reveal the inherent uncertainty of water-quality information. The applicability of Shannon entropy for signaling possible coastal pollution events in the YOIP was explored and results showed that it provides new insight into the inherent uncertainty or randomness of the original data. Specially, when Shannon entropy was high, multiple instable readings were observed for turbidity and salinity. This indicates that Shannon entropy may be a useful new tool for exploratory data analysis. It can be used as a supplementary indicator along with the original environmental data to signify some episodes of water-quality degradation.",
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Information theoretic perspective on coastal water-quality monitoring and management near an offshore industrial park. / Liu, Ta-Kang; Yu, Jin Li; Chen, Chung-Ling; Wei, Ping Sheng.

In: Environmental Monitoring and Assessment, Vol. 184, No. 8, 01.08.2012, p. 4725-4735.

Research output: Contribution to journalReview article

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