Reconstruction of Hydraulic Data by Machine Learning

03/04/2019
by   Corentin J. Lapeyre, et al.
0

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.

READ FULL TEXT

page 1

page 7

page 10

research
11/25/2021

Time Series Forecasting with Ensembled Stochastic Differential Equations Driven by Lévy Noise

With the fast development of modern deep learning techniques, the study ...
research
09/17/2020

Automatic deep learning for trend prediction in time series data

Recently, Deep Neural Network (DNN) algorithms have been explored for pr...
research
10/29/2018

A Statistical Simulation Method for Joint Time Series of Non-stationary Hourly Wave Parameters

Statistically simulated time series of wave parameters are required for ...
research
12/20/2017

Contemporary machine learning: a guide for practitioners in the physical sciences

Machine learning is finding increasingly broad application in the physic...
research
09/17/2020

Indoor Environment Data Time-Series Reconstruction Using Autoencoder Neural Networks

As the number of installed meters in buildings increases, there is a gro...
research
04/16/2021

Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions

The modelling of Earth observation data is a challenging problem, typica...
research
09/30/2022

New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences

The application of machine learning to physics problems is widely found ...

Please sign up or login with your details

Forgot password? Click here to reset