Deep learning methods for multi-horizon long-term forecasting of Harmful Algal Blooms

The increasing occurrence of Harmful Algal Blooms (HABs) in water systems poses significant challenges to ecological health, public safety, and economic stability globally. Deep Learning (DL) models, notably Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM), have been widely employed for HAB prediction. However, the emergence of state-of-the-art multi-horizon forecasting DL architectures such as Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) provides a novel solution for long-term HAB prediction. This study compares the performance of N-BEATS with LSTM and CNN models using high temporal granularity water quality data from As Conchas reservoir (NW Spain) to forecast chlorophyll-a (Chl-a) concentrations, a key indicator of HABs. The evaluation encompasses one-day and one-week prediction horizons, aligning with World Health Organization (WHO) HAB alert criteria. Results indicate that N-BEATS outperforms LSTM and CNN models for one-week predictions and when forecasting multiple consecutive days within a week. Furthermore, augmenting input data with additional variables does not significantly enhance predictive accuracy, challenging the assumption that complexity always improves model performance. The study also explores the transferability of trained models across different monitoring buoys within the same water body, emphasizing the adaptability and broad applicability of predictive models in diverse aquatic environments. This research underscores the potential of N-BEATS as a valuable tool for HAB prediction, particularly for longer-term forecasting.