STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction

The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on downstream water quality. Yet, streamflow modelling is difficult owing to the intricate interactions between different watershed outlets. In this paper, we argue that one possible solution to this problem is to identify the causal structure of these outlets, which would allow for the identification of crucial watershed outlets while capturing the spatiotemporally informed complex relationships leading to improved hydrological resource management. However, due to the inherent complexity of spatiotemporal causal learning problems, extending existing causal discovery methods to a whole basin is a major hurdle. To address these issues, we offer STREAMS, a new framework that uses Reinforcement Learning (RL) to optimize the search space for causal discovery and an LSTM-GCN based autoencoder to infer spatiotemporal causal features for streamflow rate prediction. We conduct extensive experiments on the Brazos river basin carried out within the scope of a US Army Corps of Engineers, Engineering With Nature Initiative project, including empirical studies of generalization performance to verify the nature of the inferred relationships.