  Numerical simulation models associated with hydraulic engineering take a wide
array of data into account to produce predictions: rainfall contribution to the
drainage basin (characterized by soil nature, infiltration capacity and
moisture), current water height in the river, topography, nature and geometry
of the river bed, etc. This data is tainted with uncertainties related to an
imperfect knowledge of the field, measurement errors on the physical parameters
calibrating the equations of physics, an approximation of the latter, etc.
These uncertainties can lead the model to overestimate or underestimate the
flow and height of the river. Moreover, complex assimilation models often
require numerous evaluations of physical solvers to evaluate these
uncertainties, limiting their use for some real-time operational applications.
In this study, we explore the possibility of building a predictor for river
height at an observation point based on drainage basin time series data. An
array of data-driven techniques is assessed for this task, including
statistical models, machine learning techniques and deep neural network
approaches. These are assessed on several metrics, offering an overview of the
possibilities related to hydraulic time-series. An important finding is that
for the same hydraulic quantity, the best predictors vary depending on whether
the data is produced using a physical model or real observations.
