Water contamination monitoring system based on big data: a case study. (29th August 2019)

• Record Type: Journal Article
• Authors: Zhang, Gaofeng; Yan, Yingnan; Tian, Yunsheng; Liu, Yang; Li, Yan; Zhou, Qingguo; Zhou, Rui; Li, Kuan-Ching
• Is Part Of: International journal of computational science and engineering. Volume 19:Number 4(2019)
• Page Start: 494
• Page End: 505
• Publication Date: 2019-08-29
• Subjects: water contamination -- big data -- monitoring and managing data centre -- MMDC -- monitoring -- data analysis; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Water plays a vital role in people's life, and individuals cannot survive without it. However, water contamination has become a serious issue with the development of industry and agriculture, and has become a threat to people's daily life. Moreover, the amount of data people need to process becomes excessively complex and huge in big data era. Hence, data management is increasingly a difficult task. There is an urgent need to develop a system to identify major changes of water quality through monitoring and managing these water quality variables. In this paper, we develop a data monitoring system named monitoring and managing data centre (MMDC) for monitoring, downloading, sharing, and time-series analysis based on big data technology. In order to reflect the real hydrological ecosystem, water quality variable data collected from Taihu Lake in China is used to verify the effectiveness of MMDC. Results show that MMDC brings effectiveness for monitoring and management of massive data. Although this investigation is focused on Taihu Lake, it is applicable as a general monitoring system for other similar natural resources.