Estimation of Inflows and Effective Channel from Satellite Observations: From Local to Hydrographic Network Scale

2020 ◽  
Author(s):  
Léo Pujol ◽  
Pierre-André Garambois ◽  
Pascal Finaud-Guyot ◽  
Jérôme Monnier ◽  
Robert Mosé ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Large Scale ◽  
Hydraulic Model ◽  
Small Scale ◽  
Model Parameters ◽  
Hydrodynamic Models ◽  
Channel Parameters ◽  
Spatially Distributed ◽  
Parameter Values ◽  
Hydrographic Network

<p>With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevations, widths and slopes observations globally, comes the need to pertinently use such data into hydrodynamic models, from the reach to hydrographic network scales. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy ([1,2], DassFlow software<sup>1</sup>), this work tackles the forward and inverse modeling capabilities of distributed channel parameters and inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. Several synthetic cases are designed to study fluvial and torrential flows signatures and assess the inference capabilities of model parameters (inflows, bathymetry, friction) given different observation patterns. Accurate inferences of both inflows and distributed channel parameters (bathymetry-friction) is achievable even with a minimum spatial observability between inflows. A sensitivity analysis of the inferences to prior hydraulic parameter values and to regularization parameters is performed. Next a real case is studied: 871km of the Negro river (Amazon basin) including complex multichannel reaches, 21 tributaries and backwater controls from major confluences. An effective modeling approach is proposed using (i) WS elevations from ENVISAT observations and dense in situ GPS flow lines, (ii) average river top widths from optical imagery, (iii) upstream and lateral flows from the MGB large-scale hydrological model [3]. The calibrated effective hydraulic model closely fits satellite altimetry observations of WS signatures and contains real-like spatial variabilities and flood wave propagations (frequential features analyzed with identifiability maps [2]). Synthetic SWOT observations are generated from the simulated flowlines and the identifiability of model parameters (579 bathymetry points, 17 friction patches and 22 upstream and lateral hydrographs) is tested using the HiVDI computational inverse method and given hydraulically coherent prior guesses and regularization parameter values. Inferences of channel parameters carried out on this fine hydraulic model applied at large scale give satisfying results considering the challenging inverse problems solved globally in space and time, even with noisy SWOT data. Inferences of spatially distributed temporal parameters (lateral inflows) give satisfying results as well, with even small scale hydrograph variations being infered accurately.</p><div> <p>This study brings insights in:</p> </div><ol><li> <p>the hydraulic visibility of multiple inflows hydrographs signature at large scale with SWOT;</p> </li> <li> <p>the simultaneous identifiability of spatially distributed channel parameters and inflows by assimilation of satellite altimetry data;</p> </li> <li> <p>the need to further taylor and scale hydrodynamic models and assimilation methods to improve potential information feedbacks to hydrological modules in integrated chains.</p> </li> </ol><div> <p><strong>References:</strong></p> </div><p>[1] Larnier, Monnier, Garambois, Verley. (2019) River discharge and bathymetry estimations from SWOT altimetry measurements.</p><p>[2] Brisset, Monnier, Garambois, Roux. (2018) On the assimilation of altimetric data in 1d Saint-Venant river flow models. AWR, doi: 10.1016/j.advwatres.2018.06.004.</p><p>[3] Paiva, Buarque, Collischonn, et al. Large-scale hydrologic and hydrodynamic modeling of the amazon river basin. WRR, doi: 10.1002/wrcr.20067.</p><p> </p><p> </p>

Coupled Thermo-Hydro-Geochemical Models of Engineered Barrier Systems: The Febex Project

2000 ◽  
Vol 663 ◽  
Author(s):  
J. Samper ◽  
R. Juncosa ◽  
V. Navarro ◽  
J. Delgado ◽  
L. Montenegro ◽  
...  
Keyword(s):  
Large Scale ◽  
Reference Model ◽  
Numerical Models ◽  
Full Scale ◽  
Small Scale ◽  
Model Parameters ◽  
In Situ Test ◽  
Wide Range ◽  
Engineered Barrier

ABSTRACTFEBEX (Full-scale Engineered Barrier EXperiment) is a demonstration and research project dealing with the bentonite engineered barrier designed for sealing and containment of waste in a high level radioactive waste repository (HLWR). It includes two main experiments: an situ full-scale test performed at Grimsel (GTS) and a mock-up test operating since February 1997 at CIEMAT facilities in Madrid (Spain) [1,2,3]. One of the objectives of FEBEX is the development and testing of conceptual and numerical models for the thermal, hydrodynamic, and geochemical (THG) processes expected to take place in engineered clay barriers. A significant improvement in coupled THG modeling of the clay barrier has been achieved both in terms of a better understanding of THG processes and more sophisticated THG computer codes. The ability of these models to reproduce the observed THG patterns in a wide range of THG conditions enhances the confidence in their prediction capabilities. Numerical THG models of heating and hydration experiments performed on small-scale lab cells provide excellent results for temperatures, water inflow and final water content in the cells [3]. Calculated concentrations at the end of the experiments reproduce most of the patterns of measured data. In general, the fit of concentrations of dissolved species is better than that of exchanged cations. These models were later used to simulate the evolution of the large-scale experiments (in situ and mock-up). Some thermo-hydrodynamic hypotheses and bentonite parameters were slightly revised during TH calibration of the mock-up test. The results of the reference model reproduce simultaneously the observed water inflows and bentonite temperatures and relative humidities. Although the model is highly sensitive to one-at-a-time variations in model parameters, the possibility of parameter combinations leading to similar fits cannot be precluded. The TH model of the “in situ” test is based on the same bentonite TH parameters and assumptions as for the “mock-up” test. Granite parameters were slightly modified during the calibration process in order to reproduce the observed thermal and hydrodynamic evolution. The reference model captures properly relative humidities and temperatures in the bentonite [3]. It also reproduces the observed spatial distribution of water pressures and temperatures in the granite. Once calibrated the TH aspects of the model, predictions of the THG evolution of both tests were performed. Data from the dismantling of the in situ test, which is planned for the summer of 2001, will provide a unique opportunity to test and validate current THG models of the EBS.

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 1.91% to 6.69%. <div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

Integrated Decisions on Online Product Image Configuration and Inventory Planning Using DPSO

2020 ◽  
Vol 12 (4) ◽  
pp. 1-20
Author(s):  
Kuan-Chung Shih ◽  
Yan-Kwang Chen ◽  
Yi-Ming Li ◽  
Chih-Teng Chen
Keyword(s):  
Large Scale ◽  
Operational Performance ◽  
Small Scale ◽  
Model Parameters ◽  
Inventory Planning ◽  
Comparison Results ◽  
The Face ◽  
Product Image ◽  
Online Stores ◽  
Dependent Demand

Integrated decisions on merchandise image display and inventory planning are closely related to operational performance of online stores. A visual-attention-dependent demand (VADD) model has been developed to support online stores make the decisions. In the face of evolving products, customer needs, and competitors in an e-commerce environment, the benefits of using VADD model depend on how fast the model runs on the computer. As a result, a discrete particle swarm optimization (DPSO) method is employed to solve the VADD model. To verify the usability and effectiveness of DPSO method, it was compared with the existing methods for large-scale, medium-scale, and small-scale problems. The comparison results show that both GA and DPSO method perform well in terms of the approximation rate, but the DPSO method takes less time than the GA method. A sensitivity is conducted to determine the model parameters that influence the above comparison result.

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

<div>Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.</div><div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

Contact Mechanics Analysis of a Rubber Piece on a Smooth Plate

2009 ◽  
Author(s):  
Kyosuke Ono
Keyword(s):  
Contact Mechanics ◽  
Elastic Deformation ◽  
Contact Area ◽  
Large Scale ◽  
Glass Plate ◽  
Applied Pressure ◽  
Small Scale ◽  
Asperity Contact ◽  
The Real ◽  
Parameter Values

In order to elucidate contact and friction characteristics of rubbers, numerical analysis of asperity contact mechanics of a rubber piece with a smooth glass plate was carried out on the basis of an asperity contact model that considers van der Waal’s (vdW) pressure. First, by ignoring vdW pressure and the elastic deformation of the mean height surface, asperity contact characteristics were analyzed using the measured Young’s modulus, and surface parameter values that could yield the measured contact area were estimated. Next, asperity contact characteristics were analyzed by considering the vdW pressure and elastic deformation of a rough sphere that is a model of a large-scale asperity having small-scale asperities. It was found that the actual contact area was similar to the measured contact area; this result could not be obtained without assuming an rms asperity height of ∼0.1 μm for the small-scale asperities. It was also found that the friction coefficient decreased with an increase in the applied pressure in the cases where the friction force is proportional to the real area of contact and to the real internal contact pressure.

Uncertainty quantification and global sensitivity analysis with dependent inputs: Application to the 2D hydraulic model of the Loire River

2020 ◽  
Author(s):  
Lucie Pheulpin ◽  
Vito Bacchi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Large Scale ◽  
Global Sensitivity Analysis ◽  
Numerical Models ◽  
Hydraulic Model ◽  
Model Parameters ◽  
Traditional Methods ◽  
Global Sensitivity ◽  
Loire River

&lt;p&gt;Hydraulic models are increasingly used to assess the flooding hazard. However, all numerical models are affected by uncertainties, related to model parameters, which can be quantified through Uncertainty Quantification (UQ) and Global Sensitivity Analysis (GSA). In traditional methods of UQ and GSA, the input parameters of the numerical models are considered to be independent which is actually rarely the case. The objective of this work is to proceed with UQ and GSA methods considering dependent inputs and comparing different methodologies. At our knowledge, there is no such application in the field of 2D hydraulic modelling.&lt;/p&gt;&lt;p&gt;At first the uncertain parameters of the hydraulic model are classified in groups of dependent parameters. Within this aim, it is then necessary to define the copulas that better represent these groups. Finally UQ and GSA based on copulas are performed. The proposed methodology is applied to the large scale 2D hydraulic model of the Loire River. However, as the model computation is high time-consuming, we used a meta-model instead of the initial model. We compared the results coming from the traditional methods of UQ and GSA (&lt;em&gt;i.e.&lt;/em&gt; without taking into account the dependencies between inputs) and the ones coming from the new methods based on copulas. The results show that the dependence between inputs should not always be neglected in UQ and GSA.&lt;/p&gt;

scholarly journals Estimating Caprock Impact on CO2 Migration in the Gassum Formation Using 2D Seismic Line Data

2021 ◽  
Author(s):  
Odd Andersen ◽  
Anja Sundal
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Flow Simulation ◽  
Small Scale ◽  
Model Parameters ◽  
Seismic Line ◽  
Migration Direction ◽  
3D Data ◽  
And Migration ◽  
The Impact

AbstractRealizable CO2 storage potential for saline formations without closed lateral boundaries depends on the combined effects of physical and chemical trapping mechanisms to prevent long-term migration out of the defined storage area. One such mechanism is the topography of the caprock surface, which may retain CO2 in structural pockets along the migration path. Past theoretical and modeling studies suggest that even traps too small to be accurately described by seismic data may play a significant role. In this study, we use real but scarce seismic data from the Gassum Formation of the Norwegian Continental shelf to estimate the impact of topographical features of the top seal in limiting CO2 migration. We seek to estimate the amount of macro- and sub-scale trapping potential of the formation based on a few dozen interpreted 2D seismic lines and identified faults. We generate multiple high-resolution realizations of the top surface, constructed to be faithful to both large-scale topography and small-scale statistical properties. The structural trapping and plume retardation potential of these top surfaces is subsequently estimated using spill-point (static) analysis and dynamical flow simulation. By applying these techniques on a large ensemble of top surface realizations generated using a combination of stochastic realizations and systematic variation of key model parameters, we explore the range of possible impacts on plume advancement, physical trapping and migration direction. The stochastic analysis of trapping capacity and retardation efficiency in statistically generated, sub-seismic resolution features may also be applied for surfaces generated from 3D data.

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

<div>Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.</div><div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

scholarly journals Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

2021 ◽  
Author(s):  
Raymond Pavloski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Small Scale ◽  
Engagement Theory ◽  
Sense Element ◽  
Neural Interactions ◽  
Network Simulations ◽  
New Research ◽  
Parameter Values

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

<div>Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.</div><div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

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