Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction

•A novel hybrid MARS-BWOA-SVR model is proposed for blasting vibration prediction.•Feature selection is performed based on multivariate adaptive regression splines.•Using a black widow optimization algorithm for hyperparameter optimization.•The hybrid MARS-BWOA-SVR model outperforms other SVR models...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 218; s. 113106
Hlavní autoři: Xu, Guoquan, Wang, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.08.2023
Témata:
Blasting
Ground vibration
Hybrid model
Machine learning
MARS-BWOA-SVR
MARS-BWOA-SVR
Hybrid model
Blasting
Ground vibration
Machine learning
ISSN:0263-2241
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Shrnutí:•A novel hybrid MARS-BWOA-SVR model is proposed for blasting vibration prediction.•Feature selection is performed based on multivariate adaptive regression splines.•Using a black widow optimization algorithm for hyperparameter optimization.•The hybrid MARS-BWOA-SVR model outperforms other SVR models.•Relative importance of selected input variables is assessed by MARS. Ground vibration induced by mine blasting is the most significant adverse effect on nearby residents and surroundings. Accurate prediction of blasting vibration using limited monitor data is a viable option to control ground vibration. This study proposed a novel integration modeling method based on multivariate adaptive regression splines (MARS), support vector regression (SVR) and black widow optimization algorithm (BWOA) for predicting the peak particle velocity (PPV) and frequency. Feature selection was implemented first using the MARS. Subsequently, the variables selected by the MARS were used as the input to build the SVR model. To increase the performance of the SVR model, we introduced the BWOA to tune the hyperparameters of the SVR model. Moreover, two hybrid SVR models also were developed to compare with the hybrid MARS-BWOA-SVR model. The results indicate that the prediction accuracy of the three hybrid models is superior to the standalone SVR model. Among the three hybrid models, the MARS-BWOA-SVR model yields the highest prediction accuracy. The hybrid MARS-BWOA-SVR model not only outperforms other SVR models but also discerns the relative importance of the input variables. There are four main variables with higher relative importance, namely, distance, maximum charge per delay, integrity coefficient and pre-split penetration ratio. The results demonstrate that the hybrid MARS-BWOA-SVR model is a promising tool for blasting vibration prediction.
ISSN:0263-2241
DOI:10.1016/j.measurement.2023.113106