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Porosity/Cement Index and Machine Learning Models for Predicting Tensile and Compressive Strength of Cemented Silt in Varying Compaction Conditions. [PDF]

Materials (Basel)
Baldovino JA, Coronado-Hernández OE, Nuñez de la Rosa YE.   +2 more
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Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey

Astrophysics and Space Science, 2020
This study aims to predict daily ionospheric Total Electron Content (TEC) using Gaussian Process Regression (GPR) model and Multiple Linear Regression (MLR). In this case, daily TEC values from 2015 to 2017 of two Global Navigation Satellite System (GNSS) stations were collected in Turkey.
Samed Inyurt, Aliihsan Sekertekin, Inyurt Samed   +2 more
exaly   +2 more sources

On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach

Journal of Energy Storage, 2021
Abstract To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of nanofluids via computational approaches has gained attention as an effective method to eliminate the time-consuming ...
Ismail Adewale Olumegbon, Amin Asadi, Masoud Karbasi   +2 more
exaly   +2 more sources

Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)

Geotechnical and Geological Engineering, 2019
Friction capacity (fs) of driven pile in clay is key parameter for designing pile foundation. This study employs Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR) for determination of fs of driven piles in clay. GPR is a Bayesian nonparametric regression model. MPMR is a probabilistic model.
Pijush Samui, Samui Pijush
exaly   +2 more sources

H-GPR: A Hybrid Strategy for Large-Scale Gaussian Process Regression

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
With the massive volume of data emerging from both scientific and industrial domains, it has become a desideratum to improve the scalability of Gaussian process regression (GPR). There are two major approaches to assuage its $\mathcal{O}\left( {{n^3}} \right)$ training complexity: the aggregation based methods and the sparse approximation methods. This
Naiqi Li, Yinghua Gao, Wenjie Li 0008, Yong Jiang 0001, Shu-Tao Xia   +4 more
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Determining kinetic parameters of cellulose and lignin pyrolysis by Gaussian process regression (GPR) method

2022
The ignition and flame-spread processes in the forest and urban fires involve the pyrolysis reactions of biomass materials. One of the most common methods for estimating the fire performance of a material is the evaluation of kinetic parameters, i.e., activation energy (𝐸), pre-exponential factor (𝐴), and reaction model (𝑓(𝛼)), from thermogravimetric ...
Viriya-amornkij, Pichayaporn, Kuwana, Kazunori   +1 more
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ASS-GPR: Adaptive Sequential Sampling Method Based on Gaussian Process Regression for Reliability Analysis of Complex Geotechnical Engineering

International Journal of Geomechanics, 2021
Abstract Reliability analysis of complex geotechnical engineering is time-consuming since its performance function is highly nonlinear and implicit.
Mengyao Li, Gang Wang, Long Qian, Xiangpeng Li, Zhenyue Ma   +4 more
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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

Environmental Science and Pollution Research, 2023
Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future ...
Ahmed Elbeltagi, Chaitanya B. Pande, Manish Kumar, Abebe Debele Tolche, Sudhir Kumar Singh, Akshay Kumar, Dinesh Kumar Vishwakarma   +6 more
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Drought Forecasting Using Gaussian Process Regression (GPR) and Empirical Wavelet Transform (EWT)-GPR in Gua Musang

2019
Drought forecasting is important in preparing for drought and its mitigation plan. This study focuses on the investigating the performance of Gaussian Process Regression (GPR) and Empirical Wavelet Transform-Gaussian Process Regression (EWT-GPR) in forecasting drought using Standard Precipitation Index (SPI).
Muhammad Akram Shaari, Ruhaidah Samsudin, Ani Shabri Ilman, Abdulsamad E. Yahya   +3 more
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Non-Modellable Risk Factor (NMRF) Measurement Using Gaussian Process Regression (GPR)

SSRN Electronic Journal, 2018
One innovation defined in the new market risk rules by the Fundamental Review of the Trading Book (FRTB) is the Non-Modellable Risk Factor (NMRF) framework. This new concept introduces a methodology to differentiate between modellable and non-modellable risk factors in the Internal Models Approach (IMA).
openaire   +1 more source