Real-time Forecasting of Tidal Saltwater Intrusion in the Pearl River Estuary Based on Machine Learning

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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Bibliographic Details
Main Authors: YI Jingjing, LIU Dawei, ZHU Yuke, LIU Bingjun
Format: Article
Language:zho
Published: Editorial Office of Pearl River 2025-01-01
Series:Renmin Zhujiang
Subjects:
tidal saltwater intrusion forecasting
LSTM model
GRU model
deep learning
Pearl River Estuary
Online Access:http://www.renminzhujiang.cn/thesisDetails?columnId=101738875&Fpath=home&index=0
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Summary: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.
ISSN:1001-9235

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