Assessment of Selected Machine Learning Models for Intelligent Classification of Flyrock Hazard in an Open Pit Mine

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  • Krop, Ian
    九州大学大学院工学研究院地球資源システム工学専攻岩盤・開発機械システム工学研究室
  • 高橋, 良尭
    産業技術総合研究所安全科学研究部門
  • 笹岡, 孝司
    九州大学大学院工学研究院地球資源システム工学部門岩盤・開発機械システム工学研究室
  • 島田, 英樹
    九州大学大学院工学研究院地球資源システム工学部門岩盤・開発機械システム工学研究室
  • 濵中, 晃弘
    九州大学大学院工学研究院地球資源システム工学部門岩盤・開発機械システム工学研究室
  • Onyango, Joan
    九州大学大学院工学研究院地球資源システム工学専攻岩盤・開発機械システム工学研究室

説明

This paper presents an alternative methodology for the study of flyrock hazards in mining, utilizing Artificial Intelligence (AI) through machine learning by classification. By using distance as a delineator to denote the consequences of a blast, the models generated two classes of blasts: safe and unsafe. In this study, statistical learning models best suited for classification, that is, K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs), were used, and their classification abilities were assessed. Machine performance was evaluated using a Confusion Matrix (sensitivity and specificity) and Receiver Operating Characteristic (ROC) curve. A higher weight was assigned to the minority class (unsafe blasts). Overfitting assessment was also performed. The Wide Neural Network (WNN) demonstrated the highest classification superiority. During training and validation, 75% sensitivity, 100% specificity, and an ROC of 0.9853 were achieved. In the test phase, perfect stratification (100 %) was maintained, with an ROC of 1. The Cubic SVM exhibited 50% sensitivity, 100% specificity, and an ROC of 0.9412 during training and validation. In the test set, it achieved 100% sensitivity, 100% specificity, and a ROC of 1. Fine KNN showed 50% sensitivity, 94.1% specificity, and an ROC of 0.7206 in the validation set. The test set displayed 100% sensitivity, 100% specificity, and an ROC of 1. Conversely, Coarse DT had a higher misclassification rate, resulting in a 25% sensitivity, 76.5% specificity, and an ROC of 0.5221 during the validation phase. In the test set, it showed 50% sensitivity, 100% specificity, and an ROC of 0.75. A feedforward neural network (FNN) was designed, trained, and demonstrated to be a highly flexible classification tool. The FNN achieved an excellent classification score of 100%. These findings demonstrate the potential for the broad applicability of machine learning through classification in addressing flyrock challenges in open-pit mines.

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  • IEEE Access

    IEEE Access 12 8585-8608, 2024-01-11

    Institute of Electrical Electronics Engineers(IEEE)

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