Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods
In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/5/845 |
| _version_ | 1797614270863638528 |
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| author | Rui Xu Wenjie Wu Yanpeng Cai Hang Wan Jian Li Qin Zhu Shiming Shen |
| author_facet | Rui Xu Wenjie Wu Yanpeng Cai Hang Wan Jian Li Qin Zhu Shiming Shen |
| author_sort | Rui Xu |
| collection | DOAJ |
| description | In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, COD<sub>Mn</sub>, and NH<sub>3</sub>-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction. |
| first_indexed | 2024-03-11T07:06:18Z |
| format | Article |
| id | doaj.art-29683cded6eb4417952e5144cb7aafa2 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T07:06:18Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-29683cded6eb4417952e5144cb7aafa22023-11-17T08:53:47ZengMDPI AGWater2073-44412023-02-0115584510.3390/w15050845Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning MethodsRui Xu0Wenjie Wu1Yanpeng Cai2Hang Wan3Jian Li4Qin Zhu5Shiming Shen6School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaIn environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, COD<sub>Mn</sub>, and NH<sub>3</sub>-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction.https://www.mdpi.com/2073-4441/15/5/845candlestick theorydeep learningwater quality prediction |
| spellingShingle | Rui Xu Wenjie Wu Yanpeng Cai Hang Wan Jian Li Qin Zhu Shiming Shen Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods Water candlestick theory deep learning water quality prediction |
| title | Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods |
| title_full | Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods |
| title_fullStr | Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods |
| title_full_unstemmed | Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods |
| title_short | Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods |
| title_sort | feature extraction and prediction of water quality based on candlestick theory and deep learning methods |
| topic | candlestick theory deep learning water quality prediction |
| url | https://www.mdpi.com/2073-4441/15/5/845 |
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