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.
Highlights
With the rapid development of economy and accelerated urbanization, water pollution has become more and more serious [1]
The results show the potential of application of long short-term memory (LSTM) and deep learning in predicting drinking water quality
Our result reveals the potential of applying LSTM and deep learning to predict drinking water quality, which can provide a reliable foundation for the formulation for water source protection policies and concrete measures
Summary
With the rapid development of economy and accelerated urbanization, water pollution has become more and more serious [1]. On the basis of the historical data collected by the smart water quality monitoring systems, a water quality prediction model can establish a corresponding mapping relationship between the multi-monitoring data and the changes of water quality parameters and can predict changes of water quality status in certain future periods. The above artificial neural network models are not suitable for time-series prediction problems. Due to the good performance of long short-term memory (LSTM) models in time-series prediction, the application of LSTM in environmental research has become more and more extensive [22]. This paper proposes a drinking-water quality prediction model based on LSTM deep neural networks to predict drinking-water quality data measured by the automatic water quality monitoring station of the Guazhou Water Source in Yangzhou City and compares the predicted results with the measured data. The results show the potential of application of LSTM and deep learning in predicting drinking water quality
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