Application of CNN and Long Short-Term Memory Network in Water Quality Predicting

Tan, Wenwu; Zhang, Jianjun; Wu, Jiang; Lan, Hao; Liu, Xing; Xiao, Ke; Wang, Li; Lin, Haijun; Sun, Guang; Guo, Peng

doi:10.32604/iasc.2022.029660

Cited by 19 publications

(5 citation statements)

References 21 publications

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“…Lin et al [16] propose a civil aviation demand prediction model based on deep space-time CNN, converts time series data into route grid charts, and designs multi-layer convolutional neural networks to capture time and space dependence between user needs and query data. Tan et al [17] predict water quality by CNN and long short-term memory network. Zhan [18] proposed a time series forecasting method based on CNN.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

Xu¹,

Zou²,

Wang³

2023

Computer Systems Science and Engineering

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To protect the environment, the discharged sewage's quality must meet the state's discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water's pH value is 6.0-8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater's pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater's pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into a time series which is the input of the deep learning training model. Secondly, by analyzing and comparing relevant references, this paper believes that the CNN (Convolutional Neural Network) model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers. After alternating the combination of the convolutional layer and pooling layer, all features are integrated into a full-connected neural network. Thirdly, the number of input samples of the CNN model directly affects the prediction effect of the model. Therefore, this paper adopts the sliding window method to study the optimal size. Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers. The last full-connection layer contains two layers and 64 neurons per layer. The sliding window size selects as 12. Finally, the research has carried out data prediction based on the optimal CNN deep learning model. The predicted pH of the sewage is between 7.2 and 8.6 in this paper. The result is applied in the monitoring system platform of the "Intelligent operation and maintenance platform of the reclaimed water plant."

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Section: Related Workmentioning

confidence: 99%

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

Xu¹,

Zou²,

Wang³

2023

Computer Systems Science and Engineering

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“…CNN network to extract local features from preprocessed air quality data and transfer time series with better expressive power than original water quality information to LSTM layers for prediction [10] [11]. The selection of optimal parameters is done by adjusting the number of neurons in the LSTM network and the size and number of convolution kernels in the CNN network.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) in River Water Quality Prediction

Sekarlangit,

Widodo,

Tarno

2024

ijcsrr

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River water is a crucial natural resource utilized for various purposes, including agriculture and drinking. Human activities such as mining, industrial discharge, and improper waste management contribute to river water pollution, affecting its quality and posing risks to human health. Monitoring and predicting river water quality are essential for effective management and pollution control. The research focuses on Dissolved Oxygen (DO), and comparing of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) to developed prediction models. Evaluation of the models’ performance shows that the ANN model outperforms LSTM in predicting Dissolved Oxygen (DO) concentrations, achieving lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Although LSTM exhibits lower Mean Squared Error (MSE), the ANN model demonstrates better accuracy in minimizing the average distance between predicted and actual values. The findings suggest that ANN-based models offer good performance in river water quality prediction, with potential for further enhancement through additional variables or model architecture adjustments.

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“…Guang Sun applied it to big data graph analysis [5]. Wenwu Tan applied it in water quality predicting [6]. Xu Tan applied it in analysis of production cycle-time distribution [7].…”

Section: Introductionmentioning

confidence: 99%

Index Correlation Analysis in Water Quality Monitoring Big Data

Zhang

Song

et al. 2023

J. Phys.: Conf. Ser.

Self Cite

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Among the water quality indicators, permanganate and turbidity are important indicators to reflect the pollution status of water bodies. In order to study the correlation between the two, the water quality monitoring data of relevant water areas were obtained by designing a web crawler, and the water quality monitoring data set was constructed. After the data was cleaned, the correlation analysis was carried out. The experimental results show that there is a big difference in the correlation coefficient between the two indicators at different periods of the same monitoring point. The correlation between the two indicators in the abundant-water season is greater than that in the flat-water season, and the correlation between them in the flat-water season is greater than that in the poor-water season. Among them, there was a high positive correlation between the two indicators during the abundant-water season, and there is little correlation between them during the poor-water season.

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Application of CNN and Long Short-Term Memory Network in Water Quality Predicting

Cited by 19 publications

References 21 publications

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

A Comparison of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) in River Water Quality Prediction

Index Correlation Analysis in Water Quality Monitoring Big Data

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