Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN
Long-term dynamic monitoring of the water quality of freshwater resources is of great significance to the stable and orderly operation of human society. Most studies only use one of the measured data from the monitoring station and the remote sensing satellite data as the data source. However, a sin...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9223717/ |
| _version_ | 1818917687548444672 |
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| author | Chuhan Qi Shuo Huang Xiaofei Wang |
| author_facet | Chuhan Qi Shuo Huang Xiaofei Wang |
| author_sort | Chuhan Qi |
| collection | DOAJ |
| description | Long-term dynamic monitoring of the water quality of freshwater resources is of great significance to the stable and orderly operation of human society. Most studies only use one of the measured data from the monitoring station and the remote sensing satellite data as the data source. However, a single data source will cause inaccuracy and incompatibility of the water quality monitoring results. Few studies start from practical applications to generate digital images of water quality changes. Furthermore, the performance of shallow neural networks in water quality monitoring is not often ideal. Considering the above problems, we proposed a long short-term memory network model (LSTM) to invert four key water parameters including pondus hydrogenii (PH), dissolved oxygen (DO), chemical oxygen demand (CODMn) and ammonia-nitrogen (NH3-H). Moreover, the model was applied to the satellite images of various periods to generate the inverted image of each water quality parameter. The proposed model has exhibited excellent performance in the water quality assessment of the project, with the coefficient of determination (R<sup>2</sup>), the relative root-mean-square error (rRMSE), and the mean relative error (MRE) values of 0.83, 0.16, and 0.18, respectively. And the inverted images are also consistent with the official information. |
| first_indexed | 2024-12-20T00:38:02Z |
| format | Article |
| id | doaj.art-ab1ad90b1df34856bfc4da2bb2fa82e9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T00:38:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ab1ad90b1df34856bfc4da2bb2fa82e92022-12-21T19:59:41ZengIEEEIEEE Access2169-35362020-01-01818806818808110.1109/ACCESS.2020.30308789223717Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNNChuhan Qi0https://orcid.org/0000-0002-6137-4247Shuo Huang1Xiaofei Wang2https://orcid.org/0000-0003-0716-3243College of Electronic Engineering, Heilongjiang University, Harbin, ChinaCollege of Electronic Engineering, Heilongjiang University, Harbin, ChinaCollege of Electronic Engineering, Heilongjiang University, Harbin, ChinaLong-term dynamic monitoring of the water quality of freshwater resources is of great significance to the stable and orderly operation of human society. Most studies only use one of the measured data from the monitoring station and the remote sensing satellite data as the data source. However, a single data source will cause inaccuracy and incompatibility of the water quality monitoring results. Few studies start from practical applications to generate digital images of water quality changes. Furthermore, the performance of shallow neural networks in water quality monitoring is not often ideal. Considering the above problems, we proposed a long short-term memory network model (LSTM) to invert four key water parameters including pondus hydrogenii (PH), dissolved oxygen (DO), chemical oxygen demand (CODMn) and ammonia-nitrogen (NH3-H). Moreover, the model was applied to the satellite images of various periods to generate the inverted image of each water quality parameter. The proposed model has exhibited excellent performance in the water quality assessment of the project, with the coefficient of determination (R<sup>2</sup>), the relative root-mean-square error (rRMSE), and the mean relative error (MRE) values of 0.83, 0.16, and 0.18, respectively. And the inverted images are also consistent with the official information.https://ieeexplore.ieee.org/document/9223717/Remote sensingwater quality parameterswater quality monitoringLSTM network |
| spellingShingle | Chuhan Qi Shuo Huang Xiaofei Wang Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN IEEE Access Remote sensing water quality parameters water quality monitoring LSTM network |
| title | Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN |
| title_full | Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN |
| title_fullStr | Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN |
| title_full_unstemmed | Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN |
| title_short | Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN |
| title_sort | monitoring water quality parameters of taihu lake based on remote sensing images and lstm rnn |
| topic | Remote sensing water quality parameters water quality monitoring LSTM network |
| url | https://ieeexplore.ieee.org/document/9223717/ |
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