Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.
Journal:
Journal of environmental management
PMID:
40254001
Abstract
The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has evolved over the years due to increasing technologies, availability of high frequency data, and statistical prowess. Despite the prowess of these data driven models, they are limited by inherent model structure and uncertainty in the generating process. To overcome the limitations of data driven models, in this research, we introduced the concept of data assimilation (DA) to account for model errors and incorporate new observations into the data driven deep learning HABs prediction model. Data assimilation is a computational method that enhances the precision of predictions in dynamic systems by combining real-time observations with model forecasts. In this study, we developed 100 Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to make one-day ahead prediction of chlorophyll-a, an indicator of HABs, using high-frequency pH, temperature, specific conductivity, turbidity, dissolved oxygen, saturated dissolved oxygen, and oxidation-reduction potential (ORP) data. We used an Ensemble Kalman Filter (EnKF) approach to assimilate chlorophyll-a observations of greater confidence into the HABs prediction model. We explored different assimilation frequencies to observe the appropriate timesteps required for introducing new information into the modeling system. The results showed improved chlorophyll-a prediction, as forecasted by the system when DA is applied. We found that increasing assimilation frequency tends to provide improved chlorophyll-a prediction, with daily assimilation having RMSE of 0.03 μg/l for GRU and 0.02 μg/l for LSTM, while monthly assimilation resulted in RMSE of 3.63 μg/l for GRU and 3.59 μg/l for LSTM. The study revealed the potential application of DA strategy to enhance the accuracy and reliability of deep learning models for HABs monitoring. In the presence of new chlorophyll-a observations, findings from this research inform on the appropriate frequency to which such information can be incorporated into a HABs prediction model framework. This process ensures that the model provides timely and accurate predictions to support effective HABs management and decision-making efforts.