Environments 2025, 12(5), 158; https://doi.org/10.3390/environments12050158 (registering DOI) - 10 May 2025
Abstract
Rapid population growth and climate change have created challenges for managing water quality. Protecting water sources and devising practical solutions are essential for restoring impaired inland rivers. Traditional water quality monitoring and forecasting methods rely on labor-intensive sampling and analysis, which are often
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Rapid population growth and climate change have created challenges for managing water quality. Protecting water sources and devising practical solutions are essential for restoring impaired inland rivers. Traditional water quality monitoring and forecasting methods rely on labor-intensive sampling and analysis, which are often costly. In recent years, real-time monitoring, remote sensing, and machine learning have significantly improved the accuracy of water quality forecasting. This paper categorizes machine learning approaches into traditional, deep learning, and hybrid models, evaluating their performance in forecasting water quality parameters. In recent years, the long short-term memory (LSTMs), gated recurrent units (GRUs) and LSTM- and GRU-based hybrid models have been widely used in forecasting inland river water quality. Combining remote sensing with a real-time water quality monitoring network has enhanced data collection efficiency by capturing spatial variability within the river network, complementing the high temporal resolution of in situ measurements, and improving the overall robustness of predictive deep learning models. Additionally, leveraging weather prediction models can further enhance the accuracy of water quality forecasting and better decision-making for water resource management.
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