Predicting the short-time-scale variability of chlorophyll a in the Elbe River using a Lagrangian-based multi-criterion analog model

► An analog model integrating a growth model and an intelligent prediction algorithm was constructed. ► A similitude index was designed to distinguish different samples. ► Four impact factors were selected as model inputs and assigned appropriate weights. ► The lowest weight value was assigned to silica, and the highest weight value was assigned to chlorophyll a. ► The results of the three-day predictions were acceptable.