Extended Co-Kriging interpolation method based on multi-fidelity data

Xiao, Manyu; Zhang, Guohua; Breitkopf, Piotr; Villon, Pierre; Zhang, Weihong

doi:10.1016/j.amc.2017.10.055

Cited by 47 publications

(30 citation statements)

References 16 publications

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“…Among the various interpolation methods, the Kriging interpolation is flexible and can fully utilize the data exploratory analysis tools to improve the efficiency of spatial analysis effectively 20 . It can use the statistical characteristics of known samples to quantify the spatial autocorrelation between measurement points, highlighting the overall distribution trend, increasing the data fidelity, and has the highest prediction accuracy for normal data 50 . In this study, the Kriging interpolation method was applied to explore the spatial distribution of PM 2.5 concentration data of 110 stations during 2013 and 2017, thus reveal the PM 2.5 pollution patterns in the overall study area intuitively.…”

Section: Methodsmentioning

confidence: 99%

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Sun

Luo

et al. 2019

Sci Rep

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Fine particulate matter (PM 2.5 ) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM 2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM 2.5 . To characterize these parameters and mechanisms, the 5-year PM 2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM 2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m −3 . Evident temporal variations showed high PM 2.5 level in winter and low in summer. Spatially, PM 2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.

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Section: Methodsmentioning

confidence: 99%

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Sun

Luo

et al. 2019

Sci Rep

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“…As we decompose the structural element to the original coal seam thickness and spatial information, and combine the coal seam data and spatial information to predict unknown points, this decomposition method does not affect the prediction results. number of state pairs is 2 and the number of possible state pair transitions are 4 . If using the state pairs directly solves the transition probabilities, then the computational complexity is huge.…”

Section: Structural Element Transition Probabilitymentioning

confidence: 99%

“…Among the large amount of interpolation methods, Kriging interpolation is widely used in the field of coal mining. It takes the spatial correlation into account when dealing with data, achieving good performance in most cases [4].…”

Section: Introductionmentioning

confidence: 99%

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Kang

Zhang

et al. 2019

Applied Sciences

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Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

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“…Recently, MF surrogate model methods, such as MF Kriging and MF PCE, have drawn much attention in the field of UQ. These approaches aim at achieving accurate prediction of a QoI using combination of low‐fidelity (LF) and high‐fidelity (HF) simulations.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

Cheng

Lü

2019

Numerical Meth Engineering

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Summary In this article, hierarchical surrogate model combined with dimensionality reduction technique is investigated for uncertainty propagation of high‐dimensional problems. In the proposed method, a low‐fidelity sparse polynomial chaos expansion model is first constructed to capture the global trend of model response and exploit a low‐dimensional active subspace (AS). Then a high‐fidelity (HF) stochastic Kriging model is built on the reduced space by mapping the original high‐dimensional input onto the identified AS. The effective dimensionality of the AS is estimated by maximum likelihood estimation technique. Finally, an accurate HF surrogate model is obtained for uncertainty propagation of high‐dimensional stochastic problems. The proposed method is validated by two challenging high‐dimensional stochastic examples, and the results demonstrate that our method is effective for high‐dimensional uncertainty propagation.

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Extended Co-Kriging interpolation method based on multi-fidelity data

Cited by 47 publications

References 16 publications

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

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