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Bias adjustment of satellite rainfall data through Gaussian process regression (GPR) based on rain intensity classification in the Greater Bay Area, China
Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Satellite-based remote sensing rainfall products...
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Prediction of Uniaxial Compressive Strength Using Fully Bayesian Gaussian Process Regression (fB-GPR) with Model Class Selection
In rock, mining, and/or tunneling engineering, determination of uniaxial compressive strength (UCS) of rocks is an important and crucial task, which...
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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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Resilience-based seismic design optimization of novel link beam in a double-column bridge bent using Gaussian process regression
For bridge columns with link beams, the traditional reinforced concrete beam-column joints are susceptible to damage in near-fault ground motions,...
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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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Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling
Urban groundwater resources (GWRs) have declined substantially in recent decades, due to rapid urbanization, population growth, groundwater...
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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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Prediction of Thermal Coal Ash Behavior of South African Coals: Comparative Applications of ANN, GPR, and SVR
The coal ash fusion characteristics are a significant factor to consider while designing a boiler to match a coal or different coal range....
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Gaussian Process Regression Reviewed in the Context of Inverse Theory
AbstractWe review Gaussian process regression (GPR) and analyze it in the context of Inverse Theory—the collection of techniques used in geophysics...
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Investigating the effect of TiO2-based nanofluids in the stability of crude oil flow: parametric analysis and Gaussian process regression modeling
Study has shown that the precipitation of asphaltenes could be the most detrimental mechanism that significantly influences well productivity during...
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Bi-LSTM-GPR algorithms based on a high-density electrical method for inversing the moisture content of landslide
Soil moisture content is an essential indicator for landslide monitoring and early warning. A hybrid model of bidirectional long short-term memory...
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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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Estimation of Mean Velocity Upstream and Downstream of a Bridge Model Using Metaheuristic Regression Methods
This study compares four data-driven methods, Gaussian process regression (GPR), multivariate adaptive regression spline (MARS), M5 model tree...
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Application of MCS, GRNN, and GPR for performing the reliability analysis of rock slope
The failure of rock slopes leads to disastrous consequences and thus necessitates their reliability analysis. There are various methods to perform...
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A comparative study of total dissolved solids in water estimation models using Gaussian process regression with different kernel functions
Total dissolved solids (TDS) concentration, as an essential variable in evaluating the quality of drinking and agricultural water, represents water...
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Gauss process regression for real-time ionospheric delay estimation from GNSS observations
The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding...
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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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Prediction of geoid undulation using approaches based on GMDH, M5 model tree, MARS, GPR, and IDP
This study provides a comprehensive comparison of four different machine learning models including the group method of data handling (GMDH), M5 model...
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Probabilistic Back Analysis Based on Nadam, Bayesian, and Matrix-Variate Deep Gaussian Process for Rock Tunnels
Efficiently determining the properties of the rock mass is essential for evaluating tunnel stability in tunneling projects. The back-analysis...
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A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression
Chlorophyll-a (hereafter referred to as Chl-a) is a recognized indicator for phytoplankton abundance and biomass –hence, an effective estimation of...
