A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA-SVR, PSO-SVR, GA-SVR, FA-ANN, PSO-ANN, and GA-ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA-SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA-SVR with the coefficient of determination (R-2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO-SVR withR(2) = 0.977 and RMSE = 0.725, the FA-SVR withR(2) = 0.964 and RMSE = 0.923, the GA-SVR withR(2) = 0.957 and RMSE = 1.016, the GA-ANN withR(2) = 0.936 and RMSE = 1.252, the FA-ANN withR(2) = 0.925 and RMSE = 1.368, and the PSO-ANN withR(2) = 0.924 and RMSE = 1.366. Consequently, the MFA-SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.

Automated summary

This study examines the suitability of hybridizing different optimization algorithms with data-driven models to predict ground vibration, and compares the performance of various models through a case study, with the MFA-SVR model being the most accurate.