Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Yu, Zhi; Shi, Xiuzhi; Zhou, Jian; Huang, Rendong; Gou, Yonggang

doi:10.3390/app10176031

Cited by 11 publications

(9 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [24], GP was hybridized with particle swarm optimization (PSO) to do advanced prediction of roadway broken road zone based on data collected in China. The study inferred that the hybrid model showed the best performance with higher variance accounted for (VAF), higher coefficient of determination (R 2 ), and lower root mean squared error (RMSE) compared to the other methods such as multiple linear regression (MLR) and artificial neural network (ANN).…”

Section: Gaussian Processmentioning

confidence: 99%

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

View full text Add to dashboard Cite

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

show abstract

Section: Gaussian Processmentioning

confidence: 99%

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…e Gaussian process (GP) can find a relationship between the input variable value and the output variable value of the training datasets, and the predicted output variable value of the testing datasets can be calculated using the created relationship. According to the study of Yu et al [35], Arthur et al [79], and Fang et al [36], a GP model can be determined by the mean function m(x) and covariance function k(x, x′), and that model f(x) is defined as follows:…”

Section: Gaussian Process (Gp)mentioning

confidence: 99%

“…Meanwhile, the successful application in solving the classical engineering design problem [31], airfoil design for aero vehicles problem [31], the strength of fiber-reinforced cemented paste backfill [32], and the compressive strength of concretes [33] also proves the high capacity of SSA. Besides, the Gaussian process was found to be an effective prediction technique after be used in landslide cases [34], broken rock zone prediction [35], carbon dioxide emission prediction [36], etc. Meanwhile, the conclusion that the GP performance can be improved after using the metaheuristic algorithm has been proved after many combinations such as PSO-GP [35] and GA-PSO [34] were proposed and tested.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the Gaussian process was found to be an effective prediction technique after be used in landslide cases [34], broken rock zone prediction [35], carbon dioxide emission prediction [36], etc. Meanwhile, the conclusion that the GP performance can be improved after using the metaheuristic algorithm has been proved after many combinations such as PSO-GP [35] and GA-PSO [34] were proposed and tested. However, as far as the authors know, the combination of the SSA and Gaussian process (GP) has not to be proposed and tried in predicting PPV.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

show abstract

“…Researchers in Reference [27] mentioned that the GP is capable of decreasing the complexity of the computer vision with a finer performance of reconstructions. In addition, researchers in Reference [28] compared the performance of an improved GP model with other methods, such as multiple linear regression (MLR) and artificial neural network (ANN), in predicting roadway broken road zone. The results showed that the hybrid model gave the best performance in terms of coefficient of determination (R 2 ), root mean squared error (RMSE), and variance accounted for (VAF).…”

Section: Stochastic Process-gaussian Processmentioning

confidence: 99%

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

et al. 2021

View full text Add to dashboard Cite

This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

show abstract

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

Cited by 11 publications

References 67 publications

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

Contact Info

Product

Resources

About