Cooperative prediction method of gas emission from mining face based on feature selection and machine learning
Abstract Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientific and accurate prediction of gas emission quantity in the mining face. The collaborative prediction model was screened by precision evalua...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-07-01
|
| Series: | International Journal of Coal Science & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40789-022-00519-8 |
| _version_ | 1811293808183214080 |
|---|---|
| author | Jie Zhou Haifei Lin Hongwei Jin Shugang Li Zhenguo Yan Shiyin Huang |
| author_facet | Jie Zhou Haifei Lin Hongwei Jin Shugang Li Zhenguo Yan Shiyin Huang |
| author_sort | Jie Zhou |
| collection | DOAJ |
| description | Abstract Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientific and accurate prediction of gas emission quantity in the mining face. The collaborative prediction model was screened by precision evaluation index. Samples were pretreated by data standardization, and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods. A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations. Determination coefficient, normalized mean square error, mean absolute percentage error range, Hill coefficient, mean absolute error, and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model. As such, the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out, and seven optimized collaborative forecasting models were finally determined. Results show that the judgement coefficients, normalized mean square error, mean absolute percentage error, and Hill inequality coefficient of the 7 optimized collaborative prediction models are 0.969–0.999, 0.001–0.050, 0.004–0.057, and 0.002–0.037, respectively. The determination coefficient of the final prediction sequence, the normalized mean square error, the mean absolute percentage error, the Hill inequality coefficient, the absolute error, and the mean relative error are 0.998%, 0.003%, 0.022%, 0.010%, 0.080%, and 2.200%, respectively. The multi-parameter, multi-algorithm, multi-combination, and multi-judgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity. |
| first_indexed | 2024-04-13T05:07:01Z |
| format | Article |
| id | doaj.art-0e9c378206de46428cdaebdd794461ae |
| institution | Directory Open Access Journal |
| issn | 2095-8293 2198-7823 |
| language | English |
| last_indexed | 2024-04-13T05:07:01Z |
| publishDate | 2022-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Coal Science & Technology |
| spelling | doaj.art-0e9c378206de46428cdaebdd794461ae2022-12-22T03:01:08ZengSpringerOpenInternational Journal of Coal Science & Technology2095-82932198-78232022-07-019111210.1007/s40789-022-00519-8Cooperative prediction method of gas emission from mining face based on feature selection and machine learningJie Zhou0Haifei Lin1Hongwei Jin2Shugang Li3Zhenguo Yan4Shiyin Huang5College of Safety Science and Engineering, Xi’an University of Science and TechnologyCollege of Safety Science and Engineering, Xi’an University of Science and TechnologyCollege of Safety Science and Engineering, Xi’an University of Science and TechnologyCollege of Safety Science and Engineering, Xi’an University of Science and TechnologyCollege of Safety Science and Engineering, Xi’an University of Science and TechnologyCollege of Safety Science and Engineering, Xi’an University of Science and TechnologyAbstract Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientific and accurate prediction of gas emission quantity in the mining face. The collaborative prediction model was screened by precision evaluation index. Samples were pretreated by data standardization, and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods. A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations. Determination coefficient, normalized mean square error, mean absolute percentage error range, Hill coefficient, mean absolute error, and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model. As such, the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out, and seven optimized collaborative forecasting models were finally determined. Results show that the judgement coefficients, normalized mean square error, mean absolute percentage error, and Hill inequality coefficient of the 7 optimized collaborative prediction models are 0.969–0.999, 0.001–0.050, 0.004–0.057, and 0.002–0.037, respectively. The determination coefficient of the final prediction sequence, the normalized mean square error, the mean absolute percentage error, the Hill inequality coefficient, the absolute error, and the mean relative error are 0.998%, 0.003%, 0.022%, 0.010%, 0.080%, and 2.200%, respectively. The multi-parameter, multi-algorithm, multi-combination, and multi-judgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity.https://doi.org/10.1007/s40789-022-00519-8Gas emission predictionMachine learningFeature selectionCooperative prediction |
| spellingShingle | Jie Zhou Haifei Lin Hongwei Jin Shugang Li Zhenguo Yan Shiyin Huang Cooperative prediction method of gas emission from mining face based on feature selection and machine learning International Journal of Coal Science & Technology Gas emission prediction Machine learning Feature selection Cooperative prediction |
| title | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| title_full | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| title_fullStr | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| title_full_unstemmed | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| title_short | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| title_sort | cooperative prediction method of gas emission from mining face based on feature selection and machine learning |
| topic | Gas emission prediction Machine learning Feature selection Cooperative prediction |
| url | https://doi.org/10.1007/s40789-022-00519-8 |
| work_keys_str_mv | AT jiezhou cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning AT haifeilin cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning AT hongweijin cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning AT shugangli cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning AT zhenguoyan cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning AT shiyinhuang cooperativepredictionmethodofgasemissionfromminingfacebasedonfeatureselectionandmachinelearning |