Model-based prediction of water levels for the Great Lakes: a comparative analysis

Abstract

AbstractThis comprehensive study addresses the correlation between water levels and meteorological features, including air temperature, evaporation, and precipitation, to accurately predict water levels in lakes within the Great Lakes basin. Various models, namely multiple linear regression (MLR), nonlinear autoregressive network with exogenous inputs (NARX), Facebook Prophet (FB-Prophet), and long short-term memory (LSTM), are employed to enhance predictions of lake water levels. Results indicate that all models, except for FB-Prophet, perform well, particularly for Lakes Erie, Huron-Michigan, and Superior. However, MLR and LSTM show reduced performance for Lakes Ontario and St. Clair. NARX emerges as the top performer across all lakes, with Lakes Erie and Superior exhibiting the lowest error metrics—root mean square error (RMSE: 0.048 and 0.034), mean absolute error (MAE: 0.036 and 0.026), mean absolute percent error (MAPE: 0.021% and 0.014%), and alongside the highest R-squared value (R2: 0.977 and 0.968), respectively. Similarly, for Lake Huron-Michigan, NARX demonstrates exceptional predictive precision with an RMSE (0.029), MAE (0.022), MAPE (0.013%), and an outstanding R2 value of 0.995. Despite slightly higher error metrics, NARX consistently performs well for Lake Ontario. However, Lake St. Clair presents challenges for predictive performance across all models, with NARX maintaining relatively strong metrics with an RMSE (0.076), MAE (0.050), MAPE (0.029%), and R2 (0.953), reaffirming its position as the leading model for water level prediction in the Great Lakes basin. The findings of this study suggest that the NARX model accurately predicts water levels, providing insights for managing water resources in the Great Lakes region.