  Simulating and predicting water levels in river systems is essential for
flood warnings, hydraulic operations, and flood mitigations. In the engineering
field, tools such as HEC-RAS, MIKE, and SWMM are used to build detailed
physics-based hydrological and hydraulic computational models to simulate the
entire watershed, thereby predicting the water stage at any point in the
system. However, these physics-based models are computationally intensive,
especially for large watersheds and for longer simulations. To overcome this
problem, we train several deep learning (DL) models for use as surrogate models
to rapidly predict the water stage. The downstream stage of the Miami River in
South Florida is chosen as a case study for this paper. The dataset is from
January 1, 2010, to December 31, 2020, downloaded from the DBHYDRO database of
the South Florida Water Management District (SFWMD). Extensive experiments show
that the performance of the DL models is comparable to that of the
physics-based models, even during extreme precipitation conditions (i.e.,
tropical storms). Furthermore, we study the decline in prediction accuracy of
the DL models with an increase in prediction lengths. In order to predict the
water stage in the future, our DL models use measured variables of the river
system from the recent past as well as covariates that can be reliably
predicted in the near future. In summary, the deep learning models achieve
comparable or better error rates with at least 1000x speedup in comparison to
the physics-based models.
