노천 채석장 대기소음 예측을 위한 머신러닝 모델 개발
Development of Machine Learning Models for Predicting Air Overpressure in an Open-pit Mine
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초록
본 연구에서는 노천 채석장 발파에서 발생하는 대기소음을 예측하기 위한 머신러닝 모델을 개발하였다. 경상남도 창원시에 위치한 욕망산 노천 채석장에서 924개의 발파 데이터를 수집하였다. 각발파 데이터는 천공장, 저항선, 공간격, 지발당 최대장약량, 비장약량, 총 공수, 에멀전폭약 비율, 이격거리, 대기소음으로 구성되었다. 머신러닝 모델 개발을 위해 kNN (k-nearest neighbors), RF (random forest), XGBoost (extreme gradient boosting), DNN (deep neural network) 알고리즘을 사용했다. 훈련된 모델의 성능을 MAE(평균 절대 오차), MSE(평균 제곱 오차) 및 RMSE(평균 제곱근 오차)로 평가한 결과, RF 모델이 MAE, MSE, RMSE가 각각 4.938, 42.032, 6.483로 가장 우수한 성능을 보였다.
Abstract
In this study, machine learning models were developed to predict air overpressure resulting from blasting in an open-pit mine. A total of 924 blasting data were collected from an open-pit mine at the Mt. Yogmang located in Changwon-si, Gyeongsangnam-do, Korea. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and air overpressure. Four algorithms including k-nearest neighbors (kNN), random forest (RF), extreme gradient boosting (XGBoost) and deep neural network (DNN) were used to train the machine learning models. Mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were analyzed to evaluate the performance of the trained models. As a result, the RF model showed superior performance with MAE, MSE and RMSE of 4.938, 42.032 and 6.483, respectively.
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