Six Novel Hybrid Extreme Learning Machine–Swarm Intelligence Optimization (ELM–SIO) Models for Predicting Backbreak in Open-Pit Blasting

Abstract Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects safety mines. Therefore, rapid accurate prediction BB great significance to mine design other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed optimize extreme learning machine (ELM) model for prediction, i.e., ELM-based particle (ELM–PSO), fruit fly (ELM–FOA), whale algorithm (ELM–WOA), lion (ELM–LOA), seagull (ELM–SOA) sparrow search (ELM–SSA). In total, 234 data records from operations Sungun Iran used study, including input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) output parameter (i.e., BB). To evaluate predictive performance models initial models, indicators root mean square error (RMSE), Pearson correlation coefficient (R), determination (R 2 ), variance accounted (VAF), absolute (MAE) sum (SSE) training testing phases. The results show that ELM–LSO was best predict with RMSE 0.1129 ( R : 0.9991, 0.9981, VAF: 99.8135%, MAE: 0.0706 SSE: 2.0917) phase 0.2441 0.9949, 0.9891, 98.9806%, 0.1669 4.1710). Hence, ELM techniques combined SIO are an effective method BB.