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博碩士論文 etd-0912106-211942 詳細資訊
Title page for etd-0912106-211942
論文名稱
Title
發展時空資料補遺技術於環境監測之應用
The development of a spatial-temporal data imputation technique for the applications of environmental monitoring
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
78
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2006-07-21
繳交日期
Date of Submission
2006-09-12
關鍵字
Keywords
時空資料、環境監測資料、缺失值、資料補遺
environmental monitoring data, data imputation, missing values
統計
Statistics
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中文摘要
近年來永續發展已成為國際上的重要議題,許多永續發展指標也陸續被提出,例如海島台灣、都市台灣的指標系統等。但是當我們在研究海域環境永續指標時,赫然發現環境監測資料的缺失情況極為嚴重。資料是所有資訊的源頭,然而原始資料往往會有各式缺失值存在,由這些充滿缺失的資料所推估的結果,其準確性亦受質疑。是故,如果想進一步的分析資料且獲得正確的資訊,原始資料的處理就變得格外重要。經由研究分析大致了解環境監測資料缺失值產生的原因,例如:測量機器發生故障、檢測的樣本遭到毀壞、研究人員忘記記錄、資料合併時有記錄沒有匹配,或因資料進行整理程序造成記錄遺失等。分析時,亦發現資料的缺失狀態極為不同,例如:在同一時間點,空間資料缺失某些欄位、或是缺失部分空間點的資料,以及在同一空間點,缺失少數時間點的資料、或是缺失所有時間序列資料等。因此環境監測之缺失資料與時間和空間具有相關性,目前應用於資料補遺法大致有:針對資料型態插補、針對空間分佈關係作插補、與針對時間數列函數作插補。因為同時考慮時間與空間相關性的插補極少,所以本研究發展出一套結合時間與空間資訊之環境監測資料插補法,整合相關分析技術,提升資料補遺的正確性。
Abstract
In recent years, sustainable development has become one of the most important issues internationally. Many indicators related to sustainable development have been proposed and implemented, such as Island Taiwan and Urban Taiwan. However the missing values come along with environmental monitoring data pose serious problems when we conducted the study on building a sustainable development indicator for marine environment. Since data is the origin of the summarized information, such as indicators. Given the poor data quality caused by the missing values, there will be some doubts about the result accuracy when using such data set for estimation. It is therefore important to apply suitable data pre-processing, such that reliable information can be acquired by advanced data analysis. Several reasons cause the problem of missing value in environmental monitoring data, for example: breakdown of machines, ruin of samples, forgot recording, mismatch of records when merging data, and lost of records when processing data. The situations of missing data are also diverse, for example: in the same time of sampling, some data records at several sampling sites are partially or completely disappeared. On the contrary, partial or complete time series data are missing at the same sampling site. It is therefore obvious to see that the missing values of environmental monitoring data are both related to spatial and temporal dimensions. Currently the techniques of data imputation have been developed for certain types of data or the interpolation of missing values based on either geographic data distributions or time-series functions. To accommodate both spatial and temporal information in an analysis is rarely seen. The current study has been tried to integrate the related analysis procedures and develop a computing process using both spatial and temporal dimensions inherent in the environmental monitoring data. Such data imputation process can enhance the accuracy of estimated missing values.
目次 Table of Contents
一、緒論 1
1.1研究動機與目的 1
1.2研究流程 3
二、文獻回顧 5
2.1 環境品質監測 5
2.2 插補分析模式 6
三、研究方法 9
3.1 克利金法 9
3.2 時間序列插補 12
3.3 三維插補法 14
3.3.1 Delaunay Triangulation 14
3.3.2 線性3-D有限函數(Linear 3-D shape functions) 16
3.4 類神經網路 18
3.4.1 倒傳遞網路模式 18
3.4.2 網路模式參數 20
3.5 模式驗證 21
四、模式發展與效能測試 23
4.1 插補模式發展 23
4.2 模式一:時空插補法 31
4.3 模式二:時空類神經插補法 32
4.4 結果討論 33
4.4.1 研究資料說明 33
4.4.2 不同缺失比例插補結果 39
4.4.3 不同領域資料插補結果 45
4.4.4 尺度變化插補結果 52
五、結論與建議 68
參考文獻: 70
參考文獻 References
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