Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network

A recurrent artificial neural network was used for time series modelling of phytoplankton dynamics in the hypertrophic Nakdong River system. The model considered meteorological, hydrological and limnological parameters as input variables and chl. a concentration as output variable. It was trained and validated by means of a complex database measured from 1994 to 1998 at a study site 27 km upstream of the river mouth. The validation results for 1994 indicated that the recurrent training algorithm and a 3 days time lag of input data predict reasonably accurate the timing and magnitudes of chl. a. A comprehensive sensitivity analysis of the model revealed relationships between seasons, specific input variables and chl. a that correspond well with theoretical assumptions and literature findings.