Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.

中文翻译：

---

ORELM：预测矿山爆破中飞石的新型机器学习方法

爆炸诱发的飞石是一种危险的不良现象，可能在露天矿中发生，特别是在居民区附近进行爆破时。因此，准确确定飞石距离对确定法定危险区域具有重要意义。为此，实际需要提出一种精确的模型来预测飞石。针对该主题，本研究提出了两种机器学习模型，包括极限学习机（ELM）和异常鲁棒ELM（ORELM），用于预测飞石。据我们所知，这是首次调查在飞石预测领域中使用ORELM模型的工作。为了构建和验证提议的ELM和ORELM模型，已经从马来西亚的三个花岗岩采石场收集了包括82个数据集的数据库。另外，人工神经网络（ANN）和多元回归模型用于比较。根据结果​​，ELM和ORELM模型的性能均令人满意，与ANN和多元回归模型的性能相比，它们的性能要好得多。