Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

Zhou, Jian; Huang, Shuai; Wang, Mingzheng; Qiu, Yingui

doi:10.1007/s00366-021-01418-3

Cited by 63 publications

(26 citation statements)

References 82 publications

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“…Other search units (ω) should update their respective positions according to the current position of the best search unit. Finally, the wolves attack the prey and accomplish the goal of capturing the prey ( Zhou et al, 2021a ).…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

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Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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“…The optimized SVC model for rock mass classification is established based on SVC theory and database. The model is built and trained using the MATLAB software and the main part is based on the SVM algorithm using the LIBSVM toolbox 59 , 60 . Herein, the heuristic algorithms, such as GA, PSO and GWO, are utilized to optimize SVC, reduce prediction error, and improve computing efficiency and generalization ability.…”

Section: Database Description and Svc-based Model Developmentmentioning

confidence: 99%

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Zhou

et al. 2022

Sci Rep

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The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

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“…The two important parameters of SVM are kernel function [33] and the penalty factor [34]. These two parameters directly affect the performance of SVM [35], so finding the optimal parameters of SVM becomes the core problem of constructing a classification model. How to find the optimal parameters quickly and effectively becomes the key to the optimization algorithm.…”

Section: Sca-svm Algorithmmentioning

confidence: 99%

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Zhang

2022

Processes

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In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data.

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Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

Cited by 63 publications

References 82 publications

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

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