Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining
Background: Nowadays, as in every branch of industry, a large amount of data can be collected in mining, both in productivity and occupational safety. It is increasingly essential to transform this data into useful information for enterprises. Data mining is very useful in processing and extracting...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Safety and Health at Work |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2093791124000696 |
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| author | Bilal Altındiş Fatih Bayram |
| author_facet | Bilal Altındiş Fatih Bayram |
| author_sort | Bilal Altındiş |
| collection | DOAJ |
| description | Background: Nowadays, as in every branch of industry, a large amount of data can be collected in mining, both in productivity and occupational safety. It is increasingly essential to transform this data into useful information for enterprises. Data mining is very useful in processing and extracting useful information from the processed data. This study aims to analyze the data of occupational accidents with injuries between 2010 and 2021 in an underground hard coal mine by data mining. Methods: The injured accident data for the relevant years were organized and analyzed using data mining algorithms. These algorithms were implemented with the WEKA data mining program, an open-source application. Results: According to different test methods, k-Nearest Neighborhood and Support Vector Machine algorithms succeeded in classification and prediction. The k-Nearest Neighborhood and Support Vector Machine algorithms achieved 100% (training set) and 66% (cross-validation) performance, respectively, according to two different test methods. One of the critical phases of the study is the determination of the attributes and subclasses that are effective in the origin of accidents by association rules mining. Thus, more detailed information was obtained about the root causes of the accidents. A result of Apriori and Predictive Apriori implementations revealed that the root causes of occupational accidents according to the accident locations are the worker experience, the working hours in the shift, and the worker position. In addition, shifts, accident causes, especially monthly production, and monthly wages were also influential. Conclusions: These results are also in accordance with the actual situation in the enterprise. As a result of the research, practical suggestions were presented for evaluating occupational accidents and taking precautions. |
| format | Article |
| id | doaj-art-6d69e7bf427a4b9996eb9a5dcf040cb5 |
| institution | Kabale University |
| issn | 2093-7911 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Safety and Health at Work |
| spelling | doaj-art-6d69e7bf427a4b9996eb9a5dcf040cb52024-11-30T07:10:00ZengElsevierSafety and Health at Work2093-79112024-12-01154427434Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal MiningBilal Altındiş0Fatih Bayram1TTK Turkish Hard Coal Corporation, TürkiyeDepartment of Mining Engineering, Afyon Kocatepe University, 03200, Afyonkarahisar, Türkiye; Corresponding author. Department of Mining Engineering, Afyon Kocatepe University, 03200, Afyonkarahisar, Türkiye.Background: Nowadays, as in every branch of industry, a large amount of data can be collected in mining, both in productivity and occupational safety. It is increasingly essential to transform this data into useful information for enterprises. Data mining is very useful in processing and extracting useful information from the processed data. This study aims to analyze the data of occupational accidents with injuries between 2010 and 2021 in an underground hard coal mine by data mining. Methods: The injured accident data for the relevant years were organized and analyzed using data mining algorithms. These algorithms were implemented with the WEKA data mining program, an open-source application. Results: According to different test methods, k-Nearest Neighborhood and Support Vector Machine algorithms succeeded in classification and prediction. The k-Nearest Neighborhood and Support Vector Machine algorithms achieved 100% (training set) and 66% (cross-validation) performance, respectively, according to two different test methods. One of the critical phases of the study is the determination of the attributes and subclasses that are effective in the origin of accidents by association rules mining. Thus, more detailed information was obtained about the root causes of the accidents. A result of Apriori and Predictive Apriori implementations revealed that the root causes of occupational accidents according to the accident locations are the worker experience, the working hours in the shift, and the worker position. In addition, shifts, accident causes, especially monthly production, and monthly wages were also influential. Conclusions: These results are also in accordance with the actual situation in the enterprise. As a result of the research, practical suggestions were presented for evaluating occupational accidents and taking precautions.http://www.sciencedirect.com/science/article/pii/S2093791124000696Association rules miningData miningOccupational accidentsUnderground hard coal miningWEKA |
| spellingShingle | Bilal Altındiş Fatih Bayram Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining Safety and Health at Work Association rules mining Data mining Occupational accidents Underground hard coal mining WEKA |
| title | Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining |
| title_full | Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining |
| title_fullStr | Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining |
| title_full_unstemmed | Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining |
| title_short | Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining |
| title_sort | data mining implementations for determining root causes and precautions of occupational accidents in underground hard coal mining |
| topic | Association rules mining Data mining Occupational accidents Underground hard coal mining WEKA |
| url | http://www.sciencedirect.com/science/article/pii/S2093791124000696 |
| work_keys_str_mv | AT bilalaltındis dataminingimplementationsfordeterminingrootcausesandprecautionsofoccupationalaccidentsinundergroundhardcoalmining AT fatihbayram dataminingimplementationsfordeterminingrootcausesandprecautionsofoccupationalaccidentsinundergroundhardcoalmining |