Data-driven models for forecasting algal biomass in a large and deep reservoir.

Early warning of algal biomass is important for the preservation and management of drinking water. However, accurate prediction of algal biomass in large and deep reservoirs remains a challenge. Here, we used six years of high-frequency observations (30 min/time) to train long short-term memory (LSTM) models for forecasting chlorophyll-a concentration (C) and column-integrated C (CIC) for a large and deep Chinese reservoir (Xin'anjiang Reservoir). Five LSTM-based algal biomass forecasting models were developed, including four C models for various forecasting scales (1-hour, 3-hour, 6-hour, and 24-hour) and a CIC model (forecasting scale: 1-day). The results showed that the trained LSTM-based models can accurately predict C and CIC at reservoir scale and the root mean square error (RSME) values are less than 1.1 and 14.9 μg/L, respectively. The proposed C LSTM model outperformed the MLP, CNN, CNN-LSTM, and RNN models, with the RMSE decreasing by 2.6%, 4.8%, 5.3%, and 9.3%, respectively. Similarly, the proposed CIC LSTM model surpassed the MLP, CNN, CNN-LSTM, and RNN models, resulting in a RMSE reduction of 36.1%, 46%, 50.3%, and 52.8%, respectively. With the time lag increase, the performance of the multistep-ahead forecasting model exhibits initial improvement followed by deterioration. The best performance of the multistep-ahead forecasting model was observed when the input time length is 6-8 times the forecasting time length. Spatially, the proposed models perform better at the sites with small variations in algal biomass. On the other hand, water temperature is the most important influential factor for predicting algal biomass. Our work provides an effective tool for managers to develop preemptive measures to control algal blooms.