河口區域是充滿生產力的生態系統，含有著豐富的有機質、營養鹽和多樣性的生物。河口區域也是陸源性污染物進入沿海海域的重要管道，河口水質常受到潮汐週期的影響，退潮時陸地上的污染物藉由河川水路排入海洋。這些重要又敏感的河口，其水質變化之情形需要長期且即時的監測以瞭解其受陸源性污染物之影響。緊臨著河口的雲林離島工業區，從2006年起建立了自動水質監測系統，該系統提供高頻率且即時的沿岸海域水質資料。然而，目前所面臨的一個重要課題是如何從這些大量的水質數據中，擷取出有用的資訊以做為海域水質管理之參考依據。
本研究使用雲林離島工業區兩處自動監測站之水質監測數據，如深度、鹽度、葉綠素、溫度、濁度、溶氧和比導電度等，透過資訊理論將水質自動監測數據轉換成香穠熵值和費雪信息值。由此兩指數與原始數據之比較，來瞭解其用於解釋沿海工業區海域水質監測資料之效益，並透過相關性分析來瞭解其與原始數據間之相關性，進一步探討在水質管理上之意涵。
研究結果顯示資訊理論應用在海域即時監測上具可行性，可用以處理並簡化大量的自動水質監測數據，且藉由這樣的方式開啟了沿海海域水質監測的新觀點。原始數據經本方式處理後可以有效地濾除游離值，並可凸顯出原始監測資料連續變動中，較不易看出之資訊內涵。此外比起原始數據香穠熵值是不錯的監測指標，特別是對濁度、葉綠素和鹽度等項目，透過相關性分析結果亦顯示它們的香穠熵值和原始數據有顯著的相關性。本研究並針對此三項水質參數之香穠熵值提出警戒值，當超出時可能代表水質發生異常狀況，管理單位可即時到監測海域，增加檢測其他無法由自動水質監測得到之重要水質參數如重金屬、有機物等，以即時釐清造成水質異常之原因。

Estuarine area is an important interface for transporting land-based contaminants into the coastal ocean. Its water quality can be significantly affected by the tidal cycles due to the discharge of land-based pollutants from waterways to coastal area during ebb tides. In order to understand the influence of land-based source pollutants, it is important to conduct long-term real-time monitoring for these sensitive estuaries. A semi-continuous automatic water quality monitoring system has been installed in Yunlin Offshore Industrial Park, the largest industrial park in Taiwan, since 2006 to provide real-time water quality information. However, how to extract useful information out of a large quantity of raw data becomes a challenge for coastal water quality management.
In this study, information theory was applied for processing the data of several water quality parameters: water depth, salt, chlorophyll, temperature, turbidity, dissolved oxygen, and conductivity. Shannon entropy and Fisher information were determined to explore their applicability for signaling possible coastal pollution events in the YOIP. Correlation coefficient between Shannon entropy and raw monitoring data was also calculated to explore the implication of this information theory index.
Results showed that it is feasible to employ information theory for processing large volumes of water monitoring data. The method also helps to filter the outliers and reveal the information contents. In additions, Shannon entropy is a better indication than the raw monitoring data, especially for turbidity, chlorophyll and salinity. The result of the correlation analysis showed that the relationship between with Shannon entropy and these three parameters were significant. Warning criteria of Shannon entropy for these parameters was proposed in the study. The management authority may need to conduct emergency sampling for those important parameters that cannot be monitored automatically, such as heavy metals and organics, when Shannon entropy exceeds the warning criteria since it may indicate a possible pollution event. We conclude that these information contents may be a useful new tool for exploratory data analysis to signify some episodes of water quality degradation.

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