Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

Ping Liu, Jin Wang, Arun Kumar Sangaiah, Yang Xie and Xinchun Yin
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Ping Liu: School of Information Engineering, Yangzhou University, Yangzhou 225127, China
Jin Wang: School of Information Engineering, Yangzhou University, Yangzhou 225127, China
Arun Kumar Sangaiah: School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
Yang Xie: Yangzhou Municipal Bureau of Ecology and Environment, Yangzhou 225007, China
Xinchun Yin: Guangling College, Yangzhou University, Yangzhou 225000, China

Sustainability, 2019, vol. 11, issue 7, 1-14

Abstract: This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Keywords: IoT; big data; LSTM; prediction model; water quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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