TY - JOUR
T1 - Assessment of Selected Machine Learning Models for Intelligent Classification of Flyrock Hazard in an Open Pit Mine
AU - Krop, Ian
AU - Takahashi, Yoshiaki
AU - Sasaoka, Takashi
AU - Shimada, Hideki
AU - Hamanaka, Akihiro
AU - Onyango, Joan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Algorithm
KW - artificial intelligence
KW - classification
KW - flyrocks
KW - machine learning
KW - neural networks
KW - prediction
KW - regression
KW - rock blasting
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U2 - 10.1109/ACCESS.2024.3352733
DO - 10.1109/ACCESS.2024.3352733
M3 - Article
AN - SCOPUS:85182921646
SN - 2169-3536
VL - 12
SP - 8585
EP - 8608
JO - IEEE Access
JF - IEEE Access
ER -