Reconstruction of Hydraulic Data by Machine Learning

TL;DR

This paper investigates machine learning methods to predict river height from drainage basin data, aiming to improve real-time hydraulic modeling despite uncertainties and measurement errors.

Contribution

It evaluates various data-driven models, including neural networks, for hydraulic data reconstruction, highlighting the variability in optimal predictors based on data source.

Findings

Machine learning models can effectively predict river height from basin data.

Best predictors differ between physical model data and real observations.

Deep neural networks show promising results in hydraulic data estimation.

Abstract

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.