Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Bharat Coking Coal Limited, Dhanbad, 826004, India.
Sci Rep. 2023 Apr 15;13(1):6181. doi: 10.1038/s41598-023-32693-3.
The degree to which an individual is willing to take risks i.e., risk tolerance is often cited as a significant causal element in the majority of workplace accidents. It is essential to determine the risk tolerance level of miners and utilise their risk profiles to design improved training modules, safety, recruitment, and deployment policies. This paper aims to identify the most critical factors (or features) influencing miners' risk tolerance in the Indian coal industry and develop a robust prediction model to learn their risk tolerance levels. To do end, we first conducted a questionnaire survey representing the complete feature set (with 36 features) among 360 miners and divided their responses into five classes of risk tolerance. Next, we propose a wrapper based hybrid system that combines particle swarm optimization (PSO) and random forest (RF) to train a multi-class classifier with a subset of features. In general, the proposed system selects the best feature subset by iteratively generating different feature combinations using the PSO and training an RF classifier model to assess the effectiveness of the generated feature subsets for the F1-score. At last, we compared the PSO-RF with four traditional classification methods to evaluate its effectiveness in terms of precision, recall, F1-score, accuracy, goodness-of-fit, and area under the curve.
个体愿意承担风险的程度，即风险承受能力，通常被认为是大多数工作场所事故的重要因果因素。确定矿工的风险承受能力水平并利用他们的风险概况来设计改进的培训模块、安全、招聘和部署政策至关重要。本文旨在确定影响印度煤炭行业矿工风险承受能力的最关键因素（或特征），并开发一个强大的预测模型来学习他们的风险承受能力水平。为此，我们首先在 360 名矿工中进行了一项问卷调查，代表了完整的特征集（有 36 个特征），并将他们的回答分为五个风险承受能力等级。接下来，我们提出了一种基于包装的混合系统，该系统结合了粒子群优化（PSO）和随机森林（RF），使用特征子集训练多类分类器。一般来说，该系统通过使用 PSO 生成不同的特征组合，并使用 RF 分类器模型来评估生成的特征子集对 F1 分数的有效性，从而迭代地选择最佳特征子集。最后，我们将 PSO-RF 与四种传统分类方法进行了比较，以评估其在精度、召回率、F1 分数、准确性、拟合优度和曲线下面积方面的有效性。
