Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer

Blasting is essential for breaking hard rock in opencast mines and tunneling projects. It creates an adverse impact on flyrock. Thus, it to forecast flyrock minimize the environmental effects. The objective of this study forecast/estimate amount produced during blasting by applying three creative composite intelligent models: equilibrium optimizer-coupled extreme learning machine (EO-ELM), particle swarm optimization-based (PSO-ELM), optimization-artificial neural network (PSO-ANN). To obtain a successful conclusion, we considered 114 data parameters consisting eight inputs (hole diameter, burden, stemming length, density, charge-per-meter, powder factor (PF), blastability index (BI), weathering index), one output parameter (flyrock distance). We then compared results different models using seven performance indices. Every predictive model accomplished comparable with measured values show effectiveness developed EO-ELM, result from each run 10-times compared. average shows that EO-ELM testing (R2 = 0.97, RMSE 32.14, MAE 19.78, MAPE 20.37, NSE 0.93, VAF 93.97, A20 0.57) achieved better as PSO-ANN 0.87, 64.44, 36.02, 29.96, 0.72, 74.72, 0.33) PSO-ELM 0.88, 48.55, 26.97, 26.71, 0.84, 84.84, 0.51). Further, non-parametric test performed assess these developed. prediction PSO-ANN. did sensitivity analysis introducing new parameter, WI. Input parameters, PF BI, showed highest 0.98 each.