Study on Prediction Model of Soil Cadmium Content Moisture Content Correction Based on GWO-SVR

Ying, 陈颖 Chen; Can, 张灿 Zhang; Chunyan, 肖春艳 Xiao; Xueliang, 赵学亮 Zhao; Yanxin, 史彦新 Shi; Hui, 杨惠 Yang; Zhengying, 刘峥莹 Liu; Shaohua, 李少华 Li

doi:10.3788/aos202040.1030002

Cited by 1 publication

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“…As shown in Figure 17, the EDEM screening results show that the maximum screening efficiency is 83.24% and the minimum screening time is 12.24 s. Meanwhile, the maximum screening efficiency and minimum screening time predicted with the GWO-SVR screening optimization model under the same screening parameters are 88.37% and 11.83 s, respectively. The error of the maximum screening efficiency between predicted value and test value is 5.81%, while the error of the minimum screening time between predicted Meanwhile, according to relevant research [64], for similar datasets (with fewer data samples and higher data dimensions), the prediction model constructed with GWO-SVR has better fitting performance compared to the ENR, Lasso, RR, and SVR algorithms. Compared to PSO-SVR, GA-SVR, and SA-SVR, GWO-SVR has stronger global search capabilities and convergence details [79].…”

Section: Verification Of the Optimization Resultsmentioning

confidence: 98%

“…Meanwhile, according to relevant research [64], for similar datasets (with fewer data samples and higher data dimensions), the prediction model constructed with GWO-SVR has better fitting performance compared to the ENR, Lasso, RR, and SVR algorithms. Compared to PSO-SVR, GA-SVR, and SA-SVR, GWO-SVR has stronger global search capabilities and convergence details [79].…”

Section: Optimization Process and Results Of The Screening Parametersmentioning

confidence: 99%

“…A prediction model for landslide displacement is established according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized with the gray wolf optimizer (GWO-SVR), and the results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization [63]. Owing to the serious interference from soil moisture content in the detection techniques, such as the X-ray fluorescence spectroscopy XRF method, a support vector regression SVR correction prediction model is proposed using the grey wolf optimization GWO algorithm, and the results show that the SVR nonlinear model has a better decision coefficient and smaller errors than the linear regression model [64]. In order to detect the moisture content in green tea effectively and accurately, the dielectric technology combined with the VISSA-GWO-SVR model for nondestructive determination of the moisture content in tea is proposed, which will provide a promising tool for the moisture content detection of other agricultural products [65].…”

Section: Construction Of the Screening Parameter Prediction Modelmentioning

confidence: 97%

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Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

et al. 2023

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The optimization of screening parameters will directly improve the screening performance of vibration screens, which has been a concern of the industry. In this work, the discrete element model of wet sand and gravel particles is established, and the vibration screening process is simulated using the discrete element method (DEM). The screening efficiency and time are used as evaluation indices, and the screening parameters including amplitude, vibration frequency, vibration direction angle, screen surface inclination, the long and short half-axis ratio of the track, feeding rate, and screen surface length are investigated. The results of an orthogonal experiment and range analysis show that the amplitude, screen surface inclination, and vibration frequency are significant factors affecting screening performance. Then, the support vector regression optimized with the grey wolf optimizer (GWO-SVR) algorithm is used to model the screening data. The screening model with excellent learning and prediction ability is obtained with the Gaussian kernel function setting. Moreover, the GWO-SVR algorithm is used to optimize the screening parameters, and the screening parameters with optimal screening efficiency and time are obtained. Furthermore, the effectiveness and reliability of the optimized model are verified using the discrete element calculation. The optimization strategy proposed in this work could provide guidance for the structural design of vibration screens and screening process optimization.

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Section: Verification Of the Optimization Resultsmentioning

confidence: 98%

Section: Optimization Process and Results Of The Screening Parametersmentioning

confidence: 99%

Section: Construction Of the Screening Parameter Prediction Modelmentioning

confidence: 97%

See 1 more Smart Citation

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

et al. 2023

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Study on Prediction Model of Soil Cadmium Content Moisture Content Correction Based on GWO-SVR

Cited by 1 publication

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Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

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