Title:	STUDY ON DATA CLEANSING ALGORITHMS FOR OUTLIERS IN WATER SUPPLY SYSTEM
Author(s):	Jong Rib Kim, Gang Wook Shin, Sung Taek Hong and Dae Wook Kim
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Outlier, Data Cleansing, Water Supply, LSTM
Type:	Poster
First Page:	242
Last Page:	244
Language:	English
Cover:
Full Contents:	click to dowload
Paper Abstract:	There has been systematically sharing data between government agencies, local governments and public organizations and more public organizations are encouraged to open public data in Korea. In the water industry, research on artificial intelligence using big data, a technology related to the fourth industrial revolution, is currently being carried out actively. As a result, quality control of acquired data is necessary to secure the reliability of data by developing algorithms of data cleansing to minimize outliers. In this paper, LSTM (Long Short Term Memory) for cleansing outliers and missing data is proposed to improve data quality management.
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