Altındiş Bilal, Bayram Fatih
TTK Turkish Hard Coal Corporation, Türkiye.
Department of Mining Engineering, Afyon Kocatepe University, 03200, Afyonkarahisar, Türkiye.
Saf Health Work. 2024 Dec;15(4):427-434. doi: 10.1016/j.shaw.2024.09.003. Epub 2024 Sep 6.
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.
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.
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.
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.
如今，与每个工业领域一样，采矿行业在生产率和职业安全方面都能收集大量数据。将这些数据转化为对企业有用的信息变得越来越重要。数据挖掘在处理和从已处理的数据中提取有用信息方面非常有用。本研究旨在通过数据挖掘分析某地下硬煤矿2010年至2021年期间有人员受伤的职业事故数据。
使用数据挖掘算法对相关年份的受伤事故数据进行整理和分析。这些算法是通过开源应用程序WEKA数据挖掘程序实现的。
根据不同的测试方法，k近邻算法和支持向量机算法在分类和预测方面取得了成功。根据两种不同的测试方法，k近邻算法和支持向量机算法分别在训练集上达到了100%的性能，在交叉验证中达到了66%的性能。该研究的关键阶段之一是通过关联规则挖掘确定对事故起因有影响的属性和子类。因此，获得了关于事故根本原因的更详细信息。Apriori算法和预测Apriori算法的实施结果表明，根据事故地点，职业事故的根本原因是工人经验、轮班工作时间和工人岗位。此外，轮班、事故原因，尤其是月产量和月工资也有影响。
这些结果也与企业的实际情况相符。研究结果为评估职业事故和采取预防措施提出了切实可行的建议。
