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- Title
Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods.
- Authors
Ji Zhou; Yijun Lu; Qiong Tian; Haichuan Liu; Mahdi Hasanipanah; Jiandong Huang
- Abstract
Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm(WOA) for predicting flyrock in two surfacemines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four hybrid SVR models. After modeling, the measured and predicted flyrock values are validated with someperformance indices, suchas rootmeansquarederror (RMSE).The results revealedthat the SVR-WOAmodel has the most optimal accuracy, with an RMSE of 7.218, while the RMSEs of the KELM, GRNN, SVR-GSA, ANN, SVR-BBO, and SVR-ACO models are 10.668, 10.867, 15.305, 15.661, 16.239, and 18.228, respectively. Therefore, combiningWOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.
- Subjects
IRAN; METAHEURISTIC algorithms; ARTIFICIAL neural networks; ANT algorithms; SEARCH algorithms; BLAST effect
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2024, Vol 140, Issue 2, p1595
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2024.048398