PIRT: A Physics-Informed Red Tide Deep Learning Forecast Model Considering Causal-Inferred Predictors Selection

Abstract

In this paper, a Physics-Informed Red Tide forecast model (PIRT) considering causal-inferred predictors selection is proposed. Specifically, the directed acyclic graph-graph neural network (DAG-GNN) method is first applied to quantify the causality among multiple ocean-atmosphere-biology variables for selecting the most significant predictors of the red tides (or other chlorophyll variations). Then, the encoder-decoder model consisting of an Energy Attention Module (EAM) is built for daily red tide forecasting. The multi-sourced multivariate dataset during 2010-2020 covering the East China Sea serves to train and evaluate PIRT. The experimental results demonstrate that the predictors in the learned causal graph are closely related to the occurrence and decay of red tides, which exhibits high physical interpretability. PIRT has a superior forecasting skill, the predictions of which are with highly consistent spatial patterns especially in extreme events. The 7-lead-day forecast errors for chlorophyll are within 0.9 mg∙m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which is much better than the other models. This also indicates that PIRT can be used as a reliable tool to study the ecology of the East China Sea.