Abstract: Forecast of algal blooms is a difficult problem because it is influenced by many factors that are in complex relation. This paper for the first time presents a short-term forecast model of the algal blooms in Chaohu Lake based on the combination of high-frequency measured values of environmental factors in water with BP neural network technique. The model could accurately forecast the time of each bloom, and the correlation coefficient between the forecasted and observed values is up to 0.608 4. Further, the issues in the modeling process were analyzed such as the limitation of BP neural network, the input and output data preprocessing, network structure design, training mode selection and other aspects. Finally, a specific method to select environmental factors and its modeling scheme were determined. The proposed model could be easily used on other lakes showing its strong practicability and universality.