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The development of a spatial-temporal data imputation technique for the applications of environmental monitoring

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.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0912106-211942
Date12 September 2006
CreatorsHuang, Ya-Chen
ContributorsChien-Chung Chen, Shu-Kuang Ning, Yang-Chi Chang
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912106-211942
Rightsnot_available, Copyright information available at source archive

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