Exploring advanced hybrid approaches in Hong Kong rivers for accurate prediction of surface water quality using CNN-LSTM-GRU model

Rivers play a vital role in maintaining ecological balance. Accurate water quality predictions help manage pollution levels, protect aquatic life, and preserve biodiversity. Traditional water quality testing methods are often time-consuming and less accurate. Predictive models using machine learning and deep learning offer more precise and timely assessments, enabling proactive management strategies. This research aimed to create a dynamic system for predicting river water conditions by employing one standalone model and two hybrid models. The study analyzed four key parameters—pH, salinity, turbidity, and water temperature—across three rivers in Hong Kong, namely Kam Tin, Tung Chung, and Lam Tsuen. Data spanning 24 years (2000–2023) was utilized to forecast monthly levels of total dissolved solids (TDS). To analyze the collected data, it was partitioned into two sets: 80% for training and 20% for testing. The data was then utilized in various models, including a convolutional neural network (CNN), a long short-term memory (CNN-LSTM), a gate recurrent unit (CNN-LSTM-GRU), and a deep neural network (DNN). The performance of the models was assessed and compared using three metrics: root mean square error (RMSE), coefficient of determination (R²), and Nash-Sutcliffe (NSE). The findings indicated that the hybrid CNN-LSTM-GRU model outperformed the other two models in terms of accuracy and efficiency. This model has the potential to be applied to other rivers, enhancing the speed and effectiveness of water quality monitoring and control.