scholarly journals Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Martin Bogdan ◽  
Dominik Brugger ◽  
Wolfgang Rosenstiel ◽  
Bernd Speiser
Keyword(s):  
Gaussian Process ◽  
Diffusion Coefficients ◽  
Gaussian Process Regression ◽  
Support Vector

scholarly journals Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 4055
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Predictive Accuracy ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Support Vector ◽  
Process Data ◽  
Cutting Force Prediction ◽  
Artificial Neural Network Ann ◽  
Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

scholarly journals Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework

2021 ◽  
Vol 10 (4) ◽  
pp. 218-227
Author(s):  
Jitendra Khatti ◽  
◽  
Dr. Kamaldeep Singh Grover ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Clay Content ◽  
Geotechnical Properties ◽  
Support Vector ◽  
Analysis Tool ◽  
Artificial Neural ◽  
Properties Of Soil

The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, sand, silt, and clay content. The correlation coefficient is calculated for pair of soil datasets. The correlation shows that sand, silt, and clay content play a vital role in predicting soil's liquid limit and plasticity index. The performance of constructed AI models is compared to determine the optimum performance models. The limited datasets of soil are used in this study. Therefore, artificial neural networks and relevance vector machines could not perform well. Based on the performance of AI models, the Gaussian process regression outperformed the RA, SVM, ANNs, and RVM AI technologies. Hence, the GPR AI approach can predict the geotechnical properties of soil by gravel, sand, silt, and clay content. The Monte-Carlo global sensitivity analysis is also performed, and it is observed that the prediction of geotechnical properties of soil is affected by sand and clay content

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

2020 ◽  
Vol 38 (8) ◽  
pp. 840-850 ◽  
Author(s):  
Zeynep Ceylan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Economic Indicators ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

scholarly journals Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 177
Author(s):  
Edina Chandiwana ◽  
Caston Sigauke ◽  
Alphonce Bere
Keyword(s):  
Gaussian Process ◽  
South African ◽  
Gaussian Process Regression ◽  
Limpopo Province ◽  
Gradient Boosting ◽  
Support Vector ◽  
Small Data ◽  
Western Cape ◽  
Time Data ◽  
Minimum Enclosing Ball

Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector regression for short-term hourly global horizontal irradiance (GHI) forecasting. GPR is a powerful Bayesian non-parametric regression method that works well for small data sets and quantifies the uncertainty in the predictions. The choice of a kernel that characterises the covariance function is a crucial issue in Gaussian process regression. In this study, we adopt the minimum enclosing ball (MEB) technique. The MEB improves the forecasting power of GPR because the smaller the ball is, the shorter the training time, hence performance is robust. Forecasting of real-time data was done on two South African radiometric stations, Stellenbosch University (SUN) in a coastal area of the Western Cape Province, and the University of Venda (UNV) station in the Limpopo Province. Variables were selected using the least absolute shrinkage and selection operator via hierarchical interactions. The Bayesian approach using informative priors was used for parameter estimation. Based on the root mean square error, mean absolute error and percentage bias the results showed that the GPR model gives the most accurate predictions compared to those from gradient boosting and support vector regression models, making this study a useful tool for decision-makers and system operators in power utility companies. The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. This is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data, to the best of our knowledge.

scholarly journals Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

2019 ◽  
Author(s):  
Sevda Shabani ◽  
Saeed Samadianfard ◽  
Mohammad Taghi Sattari ◽  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Random Forest ◽  
Gaussian Process ◽  
Support Vector Regression ◽  
Nearest Neighbor ◽  
Hydrological Cycle ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Nonlinear Phenomenon ◽  
Pan Evaporation ◽  
Input Parameters

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Sign in / Sign up

Export Citation Format

Share Document