Efficient Outlier Detection of the Water Temperature Monitoring Data

摘要

The statistical information of the coastal water temperature monitoring data can be biased because of outliers and missing intervals.Though a number of outlier detection methods have been developed,their applications are very limited to the in-situ monitoring data because of the assumptions of the a prior information of the outliers and no-missing condition,and the excessive computational time for some methods.In this study,the practical robust method is developed that can be efficiently and effectively detect the outliers in case of the big-data.This model is composed of these two parts,one part is the construction part of the approximate components of the monitoring data using the robust smoothing and data re-sampling method,and the other part is the main iterative outlier detection part using the detailed components of the data estimated by the approximate components.This model is tested using the two-years 5-minute interval water temperature data in Lake Saemangeum.It can be estimated that the outlier proportion of the data is about 1.6-3.7.It shows that most of the outliers in the data are detected and removed with satisfaction by the model.In order to effectively detect and remove the outliers,the outlier detection using the long-span smoothing should be applied earlier than that using the short-span smoothing.