Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model

ABSTRACT River algal blooms pose a significant environmental threat, necessitating accurate forecasts and timely warnings for effective prevention. This study proposes a novel hybrid model, combining an external recursive long short-term memory neural network based on encoder–decoder (RLSTM-ED) with a backpropagation (BP) neural network, denoted as RLSTM-ED-BP. A dataset comprising 34,992 hydrological, climatic, and water quality (4-hourly) observations from the Hanjiang River Basin in China was divided for model training and testing. Comparative analysis with an RLSTM baseline demonstrated that the RLSTM-ED-BP model enhanced the Nash–Sutcliffe coefficient (NSE) by more than 5% and reduced the root mean square error by over 10% during the 24-h forecast horizon. The RLSTM-ED-BP model yielded NSE and threat score values exceeding 0.95 and efficiently provided early warnings for algal bloom events. The model's enhanced performance contributes to the generalizability of deep learning approaches in addressing the critical environmental challenge of algal blooms.