Comparative Analysis of Prediction Performance of Mine Subsidence Using Machine Learning Techniques

Hosang Han; Kyoik Choi; Jangwon Suh

doi:10.32390/ksmer.2022.59.3.265

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2022 Vol.59, Issue 3 Next Page

Research Paper

Comparative Analysis of Prediction Performance of Mine Subsidence Using Machine Learning Techniques 머신러닝 기법을 이용한 광산 지반침하 예측 성능의 비교분석

30 June 2022. pp. 265-275

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Abstract

In this study, the prediction of mining-induced subsidence is analyzed and compared using various machine learning models. Factors affecting the occurrence of subsidence are identified from eight and 1,730 sets of subsidence data. Five machine learning models are selected, i.e., Adaboost, artificial neural networks, the k-nearest neighbor, random forest, and the support vector machine, which are frequently used in studies related to geohazard prediction. In addition, the stacking technique is applied to five algorithms based on 10 combinations, and the predictive performance of each ensemble method is evaluated and compared. To evaluate the classification performance of the machine learning technique applied in this study, recall is used as an evaluation index, which describes the ratio of the predicted ground subsidence instead of the area under curve used previously. Based on the values of recall, the random forest demonstrates the best performance (with a recall of 0.955). The recall is expected to be a more reliable evaluation index for predicting subsidence occurrences compared with other indices.

Keywords

Mine subsidence

Machine learning

Random forest

Recall

Area Under the Curve

본 연구에서는 폐광 지역의 지반침하 발생 예측에 활용되는 다양한 머신러닝 기법의 예측 성능을 비교·분석하였다. 광산 지반침하 발생 여부에 영향을 미치는 인자는 총 8개로 구성하였고, 1,730개의 지반침하 데이터를 획득하였다. 머신러닝 기법은 지반재해 예측에 대한 문헌에서 주로 활용되는 Adaboost, ANN(Artificial Neural Network), kNN(k-Nearest Neighbor), RF(Random Forest), SVM(Support Vector Machine) 등 5개를 선정하였다. 또한, 5개 알고리즘에 Stacking 기법을 적용하여 10개 조합으로 나누어 각각의 앙상블 기법들의 예측 성능을 비교하였다. 본 연구에서 적용된 머신러닝 기법의 분류 성능 평가 지표로는 기존에 사용되던 AUC(area under the curve)가 아닌 실제로 발생된 지반침하를 맞게 예측하는 비율을 의미하는 Recall에 중점을 두었다. Recall에 기반한 결과, RF 기법이 0.955로 가장 높은 성능을 나타내었다. 이는 지반침하 발생 여부를 예측하는데 기존 기법보다 신뢰성 있는 평가 기준이 될 수 있을 것으로 기대된다.

키워드

광산 지반침하

머신러닝

랜덤포레스트

재현율

곡선하면적비율

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Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 59
No :3
Pages :265-275
Received Date : 2022-04-21
Revised Date : 2022-06-10
Accepted Date : 2022-06-27
DOI :https://doi.org/10.32390/ksmer.2022.59.3.265

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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