Prediction of sediment transport rates in gravel-bed rivers using Gaussian process regression

Roushangar, Kiyoumars; Shahnazi, Saman

doi:10.2166/hydro.2019.077

Cited by 43 publications

(10 citation statements)

References 29 publications

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“…is approach is a well-known and nonparametric method used for solving classification and regression problems. Furthermore, GPR model has been commonly employed to address several water resources concerts [44][45][46][47]. GPR combines Bayesian learning and kernel machines to form a principled and probabilistic approach to create a regression model.…”

Section: Gaussian Process Regression Rasmussen and Williamsmentioning

confidence: 99%

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Sudani

Salem

2022

Advances in Meteorology

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Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.

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Section: Gaussian Process Regression Rasmussen and Williamsmentioning

confidence: 99%

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Sudani

Salem

2022

Advances in Meteorology

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“…GPR is a non-parametric model based on the Gaussian probability distribution [20]; it can be defined as a collection of random variables, of which any finite number GP has a joint Gaussian distribution [21]. Thus, a GP is completely specified by its 2 nd order statistics,…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

Ferkous¹,

Chellali²,

Kouzou³

et al. 2021

I2M

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Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

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“…The Gaussian process regression (GPR) approach has been successfully applied in many domains, e.g., [27][28][29], but its application in geotechnical engineering is limited based on literature surveys. Considering the improved performance of GPR, it is, however, used for the first time in this study to predict the UBC of shallow foundations on cohesionless soils.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

Ahmad

Wróblewski

et al. 2021

Applied Sciences

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This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

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Prediction of sediment transport rates in gravel-bed rivers using Gaussian process regression

Cited by 43 publications

References 29 publications

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

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