To show the effectiveness of the developed EO-ELM, the result from each model run 10-times is compared. Every predictive model accomplished the results comparable with the measured values of flyrock.

We then compared the results of different models using seven different performance indices. To obtain a successful conclusion, we considered 114 blasting data parameters consisting of eight inputs (hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blastability index (BI), and weathering index), and one output parameter (flyrock distance).

The objective of this study is to forecast/estimate the amount of flyrock produced during blasting by applying three creative composite intelligent models: equilibrium optimizer-coupled extreme learning machine (EO-ELM), particle swarm optimization-based extreme learning machine (PSO-ELM), and particle swarm optimization-artificial neural network (PSO-ANN). Thus, it is essential to forecast flyrock to minimize the environmental effects. Blasting is essential for breaking hard rock in opencast mines and tunneling projects.

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