Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor
This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1285 |
| _version_ | 1797297014742974464 |
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| author | Qiang Wang Tao Cheng Yijun Lu Haichuan Liu Runhua Zhang Jiandong Huang |
| author_facet | Qiang Wang Tao Cheng Yijun Lu Haichuan Liu Runhua Zhang Jiandong Huang |
| author_sort | Qiang Wang |
| collection | DOAJ |
| description | This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations. |
| first_indexed | 2024-03-07T22:14:05Z |
| format | Article |
| id | doaj.art-904198921a314e3d92fe0e193c51c64a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:14:05Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-904198921a314e3d92fe0e193c51c64a2024-02-23T15:34:03ZengMDPI AGSensors1424-82202024-02-01244128510.3390/s24041285Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best SensorQiang Wang0Tao Cheng1Yijun Lu2Haichuan Liu3Runhua Zhang4Jiandong Huang5School of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Civil Engineering, Hubei Polytechnic University, Huangshi 435003, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaDepartment of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USASchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaThis research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.https://www.mdpi.com/1424-8220/24/4/1285underground mine safetysafetyMERECCoCoSoMCDMproximity sensors |
| spellingShingle | Qiang Wang Tao Cheng Yijun Lu Haichuan Liu Runhua Zhang Jiandong Huang Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor Sensors underground mine safety safety MEREC CoCoSo MCDM proximity sensors |
| title | Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor |
| title_full | Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor |
| title_fullStr | Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor |
| title_full_unstemmed | Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor |
| title_short | Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor |
| title_sort | underground mine safety and health a hybrid merec cocoso system for the selection of best sensor |
| topic | underground mine safety safety MEREC CoCoSo MCDM proximity sensors |
| url | https://www.mdpi.com/1424-8220/24/4/1285 |
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