Improving real-time forecasting of bay water quality by integrating in-situ monitoring, machining learning, and...

Frequent occurrences of disasters such as red tides significantly threaten bay ecosystems, making near real-time water quality forecasting crucial for disaster warning and decision-making. Conventional techniques, such as process-based modeling and in-situ monitoring, struggle to achieve this for the entire bay region when applied independently. This study proposes a hybrid approach that integrates in-situ monitoring, process-based modeling, and machine learning (ML) to address this challenge. The feasibility of the approach was validated using Shenzhen Bay, a cross-boundary bay co-administered by Shenzhen and Hong Kong, as a testbed. ML models exhibited superior performance for location-specific forecasting, achieving Nash-Sutcliffe efficiency (NSE) values of 0.90, 0.84, 0.85, and 0.73 for dissolved oxygen, chlorophyll a, total nitrogen, and total phosphorus, respectively. Forecasting accuracy declined with longer lead times. Additionally, this study developed a dual-clustering method to optimize the selection of monitoring locations, minimizing the number of sites while effectively capturing the water quality across the entire bay. The results suggest that a monitoring network consisting of just two locations can adequately represent the overall water quality conditions within Shenzhen Bay. Using output from the Delft-3D model built for Shenzhen Bay, ML-based surrogate models successfully extended water quality forecasting from the two strategically selected locations to the entire bay area, with NSE values exceeding 0.8 in most regions. The hybrid approach provides a methodological foundation for achieving near real-time water quality forecasting across the entire bay areas, contributing to maintaining water security and preserving valuable ecosystem services in a rapidly changing environment.