Introducing new outlier detection method using robust statistical distance in water quality data

2019 ◽  
Vol 149 ◽  
pp. 157-163
Author(s):  
Sukmin Yoon ◽  
Seong-Su Kim ◽  
Seon-Ha Chae ◽  
No-Suk Park
Keyword(s):  
Water Quality ◽  
Outlier Detection ◽  
Detection Method ◽  
Quality Data ◽  
Water Quality Data ◽  
Statistical Distance

scholarly journals Water Quality Data Outlier Detection Method Based on Spatial Series Features

2020 ◽  
Author(s):  
Jianzhuo Yan ◽  
Ya Gao ◽  
Yongchuan Yu
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Outlier Detection ◽  
Detection Method ◽  
Quality Data ◽  
Nearest Neighbour ◽  
Water Quality Data ◽  
Spatial Series ◽  
Water Field ◽  
Major Branch

Outlier detection is one of the major branch in data mining which has been applied in different fields. Researchers have focused on the outlier detection in time series, but rarely spatial series. In this paper, we propose a new outlier detection method based on k-nearest neighbour (KNN) and Mahalanobis distance, which is first applied to the water field. Experimental results verify that the algorithm has good accuracy and effectiveness in outlier detection for water quality spatial series dataset.

scholarly journals Application of Isolation Forest Technique for Outlier Detection in Water Quality Data

2018 ◽  
Vol 40 (12) ◽  
pp. 473-480
Author(s):  
Jongeun Kim ◽  
No-Suk Park ◽  
Sangjin Yun ◽  
Seon-Ha Chae ◽  
Sukmin Yoon
Keyword(s):  
Water Quality ◽  
Outlier Detection ◽  
Quality Data ◽  
Water Quality Data ◽  
Isolation Forest

Water-quality data collected at Lake Anne, Reston, Virginia, 1997-1999

2000 ◽  
Author(s):  
Kathryn M. Conko ◽  
Margaret M. Kennedy ◽  
Karen C. Rice
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data
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