Predicting eutrophication effects in the Burrinjuck Reservoir (Australia) by means of the deterministic model SALMO and the recurrent neural network model ANNA

Two modelling paradigms were applied to the prediction of phytoplankton abundance in the Burrinjuck Reservoir: the deductive model SALMO and the inductive model ANNA. While SALMO is driven by process-based differential equations, the model ANNA is designed as recurrent feedforward neural network trained by time series data. Predictions of chlorophyll-a for the years 1979–1982 by both models were validated by means of measured data. Results showed that SALMO is able to predict annual average trends not only of chlorophyll-a but other chemical and biological state variables as well. It supports decision making by evaluating alternative scenarios for strategic eutrophication control. The model ANNA achieved reasonable accuracy in predicting timing and magnitudes of algal biomass up to 7 days ahead. The recurrent feedforward architecture of ANNA proved to be most efficient in order to model and predict seasonal dynamics of chlorophyll-a and its forecasting results can be utilized for early warning and tactical control of algal blooms in freshwater lakes. A sensitivity analysis conducted by ANNA revealed that algal abundance in Burrinjuck Reservoir is not only driven by physical and chemical characteristics of the water body but to a large extend by hydrological characteristics such as water depth as well.