Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

Abstract. With increasing lake monitoring data, data-driven machine

learning (ML) models might be able to capture the complex algal bloom

dynamics that cannot be completely described in process-based (PB) models.

We applied two ML models, the gradient boost regressor (GBR) and long short-term

memory (LSTM) network, to predict algal blooms and seasonal changes in algal

chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive

workflows were tested, one based solely on available measurements and the

others applying a two-step approach, first estimating lake nutrients that

have limited observations and then predicting Chl using observed and

pre-generated environmental factors. The third workflow was developed

using hydrodynamic data derived from a PB model as additional training

features in the two-step ML approach. The performance of the ML models was

superior to a PB model in predicting nutrients and Chl. The hybrid model

further improved the prediction of the timing and magnitude of algal blooms.

A data sparsity test based on shuffling the order of training and testing

years showed the accuracy of ML models decreased with increasing sample

interval, and model performance varied with training–testing year

combinations.