Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning
Intensified global climate change and human activities lead to increasingly severe tidal saltwater intrusion in the Pearl River Estuary, and the water supply security of coastal cities is under significant threat. This study employed long short-term memory (LSTM) and gated recurrent unit (GRU) netwo...
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| Format: | Article |
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Editorial Office of Pearl River
2025-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails?columnId=101738875&Fpath=home&index=0 |
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| author | YI Jingjing LIU Dawei ZHU Yuke LIU Bingjun |
| author_facet | YI Jingjing LIU Dawei ZHU Yuke LIU Bingjun |
| author_sort | YI Jingjing |
| collection | DOAJ |
| description | Intensified global climate change and human activities lead to increasingly severe tidal saltwater intrusion in the Pearl River Estuary, and the water supply security of coastal cities is under significant threat. This study employed long short-term memory (LSTM) and gated recurrent unit (GRU) networks to forecast and validate hourly salinity data at the Pinggang Station in the Modaomen Waterway from 2019 to 2023. The results indicate: ① Both the LSTM and GRU models demonstrate strong performance in forecasting tidal saltwater intrusion. Compared to the LSTM model, the GRU model exhibits higher forecasting accuracy, smaller prediction errors, and faster computational speed, with its performance advantages being more pronounced in short-term forecasts. ② The GRU model achieves a forecasting accuracy of above 0.8 for future 1–24 hours, with the accuracy for future 1–6 hours generally reaching 0.9. |
| format | Article |
| id | doaj-art-5d854a0a7fea4f669bfa8dc017d7b02c |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-5d854a0a7fea4f669bfa8dc017d7b02c2025-08-20T03:53:57ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352025-01-01110101738875Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine LearningYI JingjingLIU DaweiZHU YukeLIU BingjunIntensified global climate change and human activities lead to increasingly severe tidal saltwater intrusion in the Pearl River Estuary, and the water supply security of coastal cities is under significant threat. This study employed long short-term memory (LSTM) and gated recurrent unit (GRU) networks to forecast and validate hourly salinity data at the Pinggang Station in the Modaomen Waterway from 2019 to 2023. The results indicate: ① Both the LSTM and GRU models demonstrate strong performance in forecasting tidal saltwater intrusion. Compared to the LSTM model, the GRU model exhibits higher forecasting accuracy, smaller prediction errors, and faster computational speed, with its performance advantages being more pronounced in short-term forecasts. ② The GRU model achieves a forecasting accuracy of above 0.8 for future 1–24 hours, with the accuracy for future 1–6 hours generally reaching 0.9.http://www.renminzhujiang.cn/thesisDetails?columnId=101738875&Fpath=home&index=0tidal saltwater intrusion forecastingLSTM modelGRU modeldeep learningPearl River Estuary |
| spellingShingle | YI Jingjing LIU Dawei ZHU Yuke LIU Bingjun Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning Renmin Zhujiang tidal saltwater intrusion forecasting LSTM model GRU model deep learning Pearl River Estuary |
| title | Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning |
| title_full | Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning |
| title_fullStr | Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning |
| title_full_unstemmed | Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning |
| title_short | Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning |
| title_sort | real time forecasting of tidal saltwater intrusion in the pearl river estuary based on machine learning |
| topic | tidal saltwater intrusion forecasting LSTM model GRU model deep learning Pearl River Estuary |
| url | http://www.renminzhujiang.cn/thesisDetails?columnId=101738875&Fpath=home&index=0 |
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