Risky Blooms: Space-Time Chlorophyll-a Analysis and Forecasting

The uncontrolled proliferation and widespread dis-semination of harmful algal blooms (HABs) have significant consequences for the environment, climate, human health, and socioeconomics. Therefore, it is crucial to conduct a comprehensive analysis and evaluate the causes and impacts of both prolonged and sudden formations of HABs in order to develop effective strategies. In addressing ecological challenges, including HABs, computer technologies have emerged as valuable tools. To gain a profound understanding of the dynamics of blooms, it is essential to comprehend their temporal and spatial scales. However, effectively modeling ecological problems has been challenging, particularly in analyzing the influence of multiple variables on a specific variable as it evolves over time. To overcome this challenge, we propose a novel approach that combines transfer entropy (TE) network inference with a graph neural network (GNN). This approach enables the simultaneous consideration of multiple variables to model the occurrences of blooms, facilitating a comprehensive analysis of the issue from both temporal and spatial perspectives and leading to accurate predictions.