Multisite algal bloom predictions in a lake using graph attention networks

The algal blooms caused by eutrophication is a major concern in water and aquatic resource management, and various attempts have been made to accurately predict it. Algal blooms and the factors influencing them exhibit high spatial variability depending on the characteristics of the water body and water flow. However, traditional machine learning and deep learning methods have limitations to account for the spatial interactions of various influencing factors across multiple monitoring sites. In addition, attempts to predict multiple sites simultaneously using a single model are limited. In this study, we proposed a model that considers spatial interactions and performs multisite predictions based on a graph attention network (GAT). The GAT–DNN, which combines a deep neural network (DNN) after GAT layer, was applied to forecast chlorophyll-a levels at multiple sites. The proposed model accurately captured the high variability and peak chlorophyll-a levels. Moreover, the GAT–DNN consistently outperformed two baseline DNNs in both cases. Additionally, we examined the optimal forecast horizon by comparing the performance of the model across various forecast horizons. Therefore, the proposed model can be applied to a wide range of prediction models to capture spatial interactions and obtain the benefits of performance outcomes for each site.