An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were const...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Minerals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-163X/11/11/1294 |
| _version_ | 1797509214953799680 |
|---|---|
| author | Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan |
| author_facet | Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan |
| author_sort | Honglei Wang |
| collection | DOAJ |
| description | Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction. |
| first_indexed | 2024-03-10T05:14:33Z |
| format | Article |
| id | doaj.art-2c9aa19e99604b91a8cadceefc489b1a |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-10T05:14:33Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-2c9aa19e99604b91a8cadceefc489b1a2023-11-23T00:33:12ZengMDPI AGMinerals2075-163X2021-11-011111129410.3390/min11111294An Intelligent Rockburst Prediction Model Based on Scorecard MethodologyHonglei Wang0Zhenlei Li1Dazhao Song2Xueqiu He3Aleksei Sobolev4Majid Khan5Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, ChinaKhabarovsk Federal Research Center of the Far Eastern Branch of Russian Academy of Science, 51 Turgenev Street, 680000 Khabarovsk, RussiaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Beijing, Beijing 100083, ChinaRockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.https://www.mdpi.com/2075-163X/11/11/1294rockburstscorecardintelligence predictioninterpretabilityclass weightsmachine learning |
| spellingShingle | Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan An Intelligent Rockburst Prediction Model Based on Scorecard Methodology Minerals rockburst scorecard intelligence prediction interpretability class weights machine learning |
| title | An Intelligent Rockburst Prediction Model Based on Scorecard Methodology |
| title_full | An Intelligent Rockburst Prediction Model Based on Scorecard Methodology |
| title_fullStr | An Intelligent Rockburst Prediction Model Based on Scorecard Methodology |
| title_full_unstemmed | An Intelligent Rockburst Prediction Model Based on Scorecard Methodology |
| title_short | An Intelligent Rockburst Prediction Model Based on Scorecard Methodology |
| title_sort | intelligent rockburst prediction model based on scorecard methodology |
| topic | rockburst scorecard intelligence prediction interpretability class weights machine learning |
| url | https://www.mdpi.com/2075-163X/11/11/1294 |
| work_keys_str_mv | AT hongleiwang anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT zhenleili anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT dazhaosong anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT xueqiuhe anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT alekseisobolev anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT majidkhan anintelligentrockburstpredictionmodelbasedonscorecardmethodology AT hongleiwang intelligentrockburstpredictionmodelbasedonscorecardmethodology AT zhenleili intelligentrockburstpredictionmodelbasedonscorecardmethodology AT dazhaosong intelligentrockburstpredictionmodelbasedonscorecardmethodology AT xueqiuhe intelligentrockburstpredictionmodelbasedonscorecardmethodology AT alekseisobolev intelligentrockburstpredictionmodelbasedonscorecardmethodology AT majidkhan intelligentrockburstpredictionmodelbasedonscorecardmethodology |