Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Abstract

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.