
Automatic deep learning for trend prediction in time series data
Recently, Deep Neural Network (DNN) algorithms have been explored for pr...
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A Statistical Simulation Method for Joint Time Series of Nonstationary Hourly Wave Parameters
Statistically simulated time series of wave parameters are required for ...
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Contemporary machine learning: a guide for practitioners in the physical sciences
Machine learning is finding increasingly broad application in the physic...
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Indoor Environment Data TimeSeries Reconstruction Using Autoencoder Neural Networks
As the number of installed meters in buildings increases, there is a gro...
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Learning ergodic averages in chaotic systems
We propose a physicsinformed machine learning method to predict the tim...
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PhysicsGuided Deep Neural Networks for PowerFlow Analysis
Solving power flow (PF) equations is the basis of power flow analysis, w...
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Big Data vs. complex physical models: a scalable inference algorithm
The data torrent unleashed by current and upcoming instruments requires ...
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Reconstruction of Hydraulic Data by Machine Learning
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 realtime 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 datadriven 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 timeseries. 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.
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