Abstract：Eutrophication and harmful algal blooms in lakes and reservoirs are global eco-environmental issues. The prediction and early warning of algal blooms are the key techniques for securing the safe drinking water supply. How to predict algal blooms in a real-time dynamic way based on high-frequency water ecology monitoring data has become a major demand in the field of aquatic ecosystem management. Taking Jiangdong reservoir of Jiulong River （i.e.， drinking water source of Xiamen in Fujian Province） as a case study， this study developed and compared the performance of three types of time series models of SARIMA， Prophet， and LSTM （long-term and short-term memory neural network） in predicting algal bloom （defined as daily average chlorophyll-a is greater than 15 μg?L-1）， using the three-year continuously observed hourly mean total chlorophyll-a concentration data. The results show that： ①the time series model requires few parameters and has strong flexibility， which reflect the water quality characteristics and future trends， and can overcome the limitations of traditional methods of algae monitoring and early warning； ②The LSTM model based on the deep learning framework has a relatively strong ability to identify and predict the nonlinear variation characteristics of algae， due to its unique iterative optimization algorithm； the LSTM performance on daily prediction and seven-day prediction of total chlorophyll-a are both better than SARIMA model and Prophet model； ③The length of input data will affect the prediction performance of the models to some extent. The optimal length of inputs in this study was identified as 7-days. The frequency of input data also has an impact on the prediction performance. When predicting non-algal bloom days， the prediction ability to use hourly data is better than that of using daily data. When predicting algal bloom days， there is no significant difference between the two-frequency data， but the daily data can more accurately capture the characteristics of algal bloom. In summary， the short-term prediction of total chlorophyll-a concentration based on the LSTM model can provide technical support for early warning of algal bloom and water supply security in the Jiulong River Reservoir.