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Effect of Different Basis Sets on the Theoretical Calculation of Zinc Isotope Fractionation of Zn Complexes
Isotope fractionation of zinc between Zn(H2O)62+, ZnCl(H2O)5+, ZnCl2(H2O)4, ZnCl3(H2O)−, ZnCl42−, ZnHCO3(H2O)3+, ZnCO3(H2O)3, Zn(OH)2(H2O)4, and...
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Geostatistical modelling of rainfall in Fars Province of Iran using non-Gaussian spatial process
Prediction of response values is a primary goal in many applications. The standard approach to this problem is kriging which is essentially a linear...
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Prediction of soil compaction parameters through the development and experimental validation of Gaussian process regression models
The laboratory determination of maximum dry density ( ρ dmax ) and optimum moisture content ( w opt ) of soils requires considerable time and energy....
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Variational quality control of non-Gaussian innovations and its parametric optimizations for the GRAPES m3DVAR system
The magnitude and distribution of observation innovations, which have an important impact on the analyzed accuracy, are critical variables in data...
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Probabilistic Back Analysis Based on Adam, Bayesian and Multi-output Gaussian Process for Deep Soft-Rock Tunnel
The uncertainty of surrounding rock mechanical parameters has much great influence on design, construction and stability evaluation for tunnel...
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Alternative methods for semi-automatic clusterization and extraction of discontinuity sets from 3D point clouds
Recent advances in the use of remote sensing techniques allow the acquisition of dense 3D information helpful for the characterization of the rock...
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Intelligent velocity picking and uncertainty analysis based on the Gaussian mixture model
The stacking velocity is often obtained manually. However, manually picking is inefficient and is easily affected by subjective factors such as the...
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Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm
The hydropower industry is one of the most important sources of clean energy. Predicting hydropower production is essential for the hydropower...
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Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall
Rainfall prediction is an important issue in water resource management. Predicting rainfall helps researchers to monitor droughts, surface water and...
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Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark
Surrogate models are widely used to improve the computational efficiency in various geophysical simulation problems by reducing the number of model...
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Basis functions for shallow-water temperature profiles based on the internal-wave eigenmodes
The shallow-water temperature profile is typically parameterized using a few empirical orthogonal function (EOF) coefficients. However, when the...
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Nonrigid Registration Using Gaussian Processes and Local Likelihood Estimation
Surface registration, the task of aligning several multidimensional point sets, is a necessary task in many scientific fields. In this work, a novel...
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Reducing the Errors of the Solar and Climatic Indices on the Basis of a Hypothesis on Nonlinear Dynamic Sun–Climate Coupling
AbstractConfidence interval estimates for the average values of the solar and climate indices are important for both modeling and forecasting...
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A Hybrid Estimation Technique Using Elliptical Radial Basis Neural Networks and Cokriging
Mineral resource estimation is an integral part of making informed decisions while evaluating a mining operation’s feasibility. Geostatistical tools...
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Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study
Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir...
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Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets
Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established...
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A Gaussian process regression-based sea surface temperature interpolation algorithm
The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models. Low-resolution...
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Vertical derivative conversion of irregular-range potential field data based on improved projection onto convex sets method
Gravity and magnetic exploration areas are usually irregular, and there is some data deficiency. Missing data must be interpolated before the...
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A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions
In this study, a new data-adaptive network design methodology called k -SRBF is presented for the spherical radial basis functions (SRBFs) in regional...
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A Gaussian model approach to determine the commencement, termination and length of the major growing season over the dry zone of Sri Lanka
The commencement, termination and length of the major ( Maha ) growing season over the dry zone of Sri Lanka were determined using daily rainfall...
