Ensemble Kalman filter regularization using leave-one-out data cross-validation

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

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Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

Applied Sciences ◽

10.3390/app11072898 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2898

Author(s):

Humberto C. Godinez ◽

Esteban Rougier

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Failure Modes ◽

Split Hopkinson Pressure Bar ◽

Peak Stress ◽

Discrete Element ◽

Material Behavior ◽

Model Parameters ◽

Hopkinson Pressure Bar ◽

Parameter Values

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

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Sparseness of the LS-SVM algorithm based on the leave one out cross validation method with Multibeam data

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering ◽

10.1145/3443467.3443763 ◽

2020 ◽

Author(s):

Bo Zhang ◽

Xianyuan Huang ◽

Long Fan ◽

Guobin Chang

Keyword(s):

Cross Validation ◽

Validation Method ◽

Svm Algorithm ◽

Leave One Out ◽

Multibeam Data

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Microseismic Monitoring of Mines in Real Time with Ensemble Kalman Filter

10.3997/2214-4609.201900758 ◽

2019 ◽

Author(s):

A.C. Dip ◽

B. Giroux ◽

E. Gloaguen

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Microseismic Monitoring

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Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽

10.3390/w11071520 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1520

Author(s):

Zheng Jiang ◽

Quanzhong Huang ◽

Gendong Li ◽

Guangyong Li

Keyword(s):

Kalman Filter ◽

Solute Transport ◽

Ensemble Kalman Filter ◽

Water Movement ◽

Parameters Estimation ◽

Saturated Soils ◽

Hydrological Models ◽

State Variables ◽

Ensemble Size ◽

Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

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Real-Time Monitoring and Interpretation of Wireline Formation Testing Using Ensemble Kalman Filter

10.4043/29245-ms ◽

2019 ◽

Author(s):

Hani Elshahawi ◽

Andrey Filippov

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Real Time Monitoring

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Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

ICES Journal of Marine Science ◽

10.1093/icesjms/fsz099 ◽

2019 ◽

Vol 76 (7) ◽

pp. 2349-2361

Author(s):

Benjamin Misiuk ◽

Trevor Bell ◽

Alec Aitken ◽

Craig J Brown ◽

Evan N Edinger

Keyword(s):

Spatial Autocorrelation ◽

Cross Validation ◽

Predictive Accuracy ◽

Accuracy Assessment ◽

Small Scale ◽

Suitable Habitat ◽

Distribution Models ◽

Management Tools ◽

Seabed Topography ◽

Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

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