GA-LSSVM prediction of blasting casting effect in open⁃pit mine based on Fourier series

The blast casting effect directly affects the stripping cost of open⁃pit mine，and has an important impact on the production efficiency of the throwing⁃blasting⁃draw bucket dumping process system. Based on the influencing factors of mine blast casting effect，a prediction model of blast casting effect using genetic algorithm (GA) optimized least squares support vector machine (LSSVM) was proposed. The Fourier series model was introduced for the first time to simulate the profile curve of the explosion，and the trained GA-LSSVM was used to predict the Fourier se⁃ ries model control parameters A0，θ，an and bn，and then output the predicted explosion shape. Based on the actu⁃ al measurement data of blast casting in the Heidaigou Open⁃pit Coal Mine， the parameters were selected， such as the height of the bench， the width of the section， the unit consumption of explosives， the minimum resistance line，the row spacing，the hole spacing，the slope angle，the widths of the upper opening and the lower opening of the gob，loose square volume，and effective throwing volume，as the input parameters of the GA-LSSVM predic⁃ tion model，and the A0，θ，an，bn，the farthest throwing distance，loosening coefficient and effective throwing rate were used as output parameters to establish the GA-LSSVM model for predicting the blast casting effect at open⁃pit mine. In addition，the LSSVM prediction model was compared with the Partial Least Squares Regression Model (PLSR)，LSS⁃ VM Model，and Particle Swarm Optimization Least Squares Support Vector Machine Model (PSO-LSSVM). The re⁃ sults show that 1 the detonation profile curve is simulated and analyzed by the Fourier expansion series of orders 2 to 7，and it is determined that the simulation accuracy and efficiency are optimal when the order is 4，the sum of squares of errors ( SSE ) is 21. 593 4， and the coefficient of determination ( R2 ) and the adjusted coefficient of determination (R2adj) is 0.999 2，and the root mean square error (RMSE) is 0.479 3; 2 compared with the tradition⁃ al LSSVM prediction model，after the GA optimization，the farthest throwing distance，the loose coefficient and the ef⁃ fective throwing rate are all higher R2 value (1，1，1) and a smaller RMSE value (0.180 9，0.000 7，0.000 2) are ob⁃ tained，indicating that the improved GA-LSSVM has better simulation effect and generalization ability; 3 compared with PSO-LSSVM，LSSVM，and PLSR，the GA-LSSVM model has a higher prediction accuracy (R2，RMSE) for the blasting effect of throwing and has obvious advantages; and 4 compared with models such as GA-ELM，the prediction of the explosion shape has higher operational efficiency and prediction accuracy.