Causally Informed Input and Lag Selection for LSTM-based Hydrological Forecasting in the Lake Erie Basin

Long Short-Term Memory (LSTM) networks have recently emerged as a leading deep learning architecture for hydrological forecasting due to their ability to represent nonlinear and long-term dependencies in time series data. However, the selection of input variables and temporal lags for LSTM networks is often heuristic and characterized by the inclusion of all available forcings and wide lag windows. This practice can yield over-parameterized models that are prone to overfitting. Causal discovery–based feature selection offers a principled alternative to heuristic input configuration. While these methods have shown promise in improving model interpretability and generalization in statistical and machine learning contexts, their integration with deep learning architectures remains underexplored. Here, we present a workflow that integrates causal inference for time series as a preprocessing step for LSTM-based hydrological forecasting in an operational hydropower context. Using subdaily multivariate hydroclimatic time series from the Lake Erie basin in Ontario, Canada, we apply the PC-MCI algorithm to infer directed causal relationships and characteristic temporal lags among streamflow, lake levels, meteorological forcings, and hydropower-relevant predictor variables. The resulting causal graphs provide a model-agnostic, interpretable basis for defining the predictor sets and lag structures that form the input configuration of an encoder–decoder LSTM model. Ongoing work evaluates whether causally informed configurations improve forecast skill and generalization relative to conventional variable‑selection strategies and assesses the computational and operational trade-offs of the proposed workflow.