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

By Ping Liu, Jin Wang, Arun Kumar Sangaiah, Yang Xie, Xinchun Yin. Sustainability, 2019

312 citations recorded by the source corpus.

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.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/bb09d58a056d1a0b6312108e725c733fd6108bc2

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In Environmental Engineering

In Water Science and Technology

Citations in the corpus

A review of the application of machine learning in water quality evaluation (2022, methodology): “In addition, Liu et al. [57] used LSTM and DNN to predict time series data and established a water quality prediction model that could predict the water quality in the next six months with relatively good accuracy.”
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “4 The structure of the hybrid CNN–LSTM model (Liu et al. 2019a) which can occur with r , RMSE , MAE and BIAS metrics (Willmott 1981; Yaseen et al. 2018).”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “…ﬁt to drink or not Heterogeneous (Chlorides, Nitrates, Totaldissolvedsolids,pHandHard-ness,andotherchemicalproperties) SVM [110] DNN [111] RNN (LSTM) [112] Regression—Prediction of water quality Sewer Overﬂow Monitoring RNN (GRU, LSTM) [108 [116] Cloud Application—Dashboard(objectidentiﬁcationetc.)”
A critical review of RNN and LSTM variants in hydrological time series predictions (2024, background): “The LSTM network can forecast drinking water’s pH, DO, COD, and NH3-N concentrations [ 51 ].”
Water quality prediction using machine learning models based on grid search method (2023, unknown): “…have been developed in counties and cities throughout China, relying on various types of Stations for spontaneous hydrological and water quality surveillance, wireless sensor networks (WSNs), RS surveillance systems, surveillance ships, and sophisticated underwater robotic machines [9].”