Analysis and Forecast of Mining Accidents in Pakistan

IF 1.1 Q3 MINING & MINERAL PROCESSING
K. S. Shah, Izhar Mithal Jiskani, Niaz Muhammad Shahani, H. Rehman, N. Khan, S. Hussain
{"title":"Analysis and Forecast of Mining Accidents in Pakistan","authors":"K. S. Shah, Izhar Mithal Jiskani, Niaz Muhammad Shahani, H. Rehman, N. Khan, S. Hussain","doi":"10.22044/JME.2020.10082.1945","DOIUrl":null,"url":null,"abstract":"In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surface and underground mining in Pakistan. The original application of the ARIMA model provides that how the number of accidents and fatalities is influenced by the implementation of various approaches to promote an effective safety management system. The ARIMA model requires the data series of the predicted elements with a random pattern over time and produce an equation. After the model identification, it may forecast the future pattern of the events based on its existing and future values. In this research work, the accident data for the period of 2006-2019-is collected from Inspectorate of Mines and Minerals (Pakistan), Mine Workers Federation, and newspapers in order to evaluate the long-term forecast. The results obtained reveal that ARIMA (2, 1, 0) is a suitable model for both the mining accidents and the workers’ fatalities. The number of accidents and fatalities are forecasted from 2020 to 2025. The results obtained suggest that the policy-makers should take a systematic consideration by evaluating the possible risks associated with an increased number of accidents and fatalities, and develop a safe and effective working platform.","PeriodicalId":45259,"journal":{"name":"Journal of Mining and Environment","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mining and Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22044/JME.2020.10082.1945","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MINING & MINERAL PROCESSING","Score":null,"Total":0}
引用次数: 5

Abstract

In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surface and underground mining in Pakistan. The original application of the ARIMA model provides that how the number of accidents and fatalities is influenced by the implementation of various approaches to promote an effective safety management system. The ARIMA model requires the data series of the predicted elements with a random pattern over time and produce an equation. After the model identification, it may forecast the future pattern of the events based on its existing and future values. In this research work, the accident data for the period of 2006-2019-is collected from Inspectorate of Mines and Minerals (Pakistan), Mine Workers Federation, and newspapers in order to evaluate the long-term forecast. The results obtained reveal that ARIMA (2, 1, 0) is a suitable model for both the mining accidents and the workers’ fatalities. The number of accidents and fatalities are forecasted from 2020 to 2025. The results obtained suggest that the policy-makers should take a systematic consideration by evaluating the possible risks associated with an increased number of accidents and fatalities, and develop a safe and effective working platform.
巴基斯坦矿山事故分析与预测
在采矿部门,获得有效安全管理制度的障碍是无法获得有关事故的未来资料。本文旨在首次使用自回归综合移动平均(ARIMA)模型来评估影响巴基斯坦地面和地下采矿事故和死亡人数对应的安全管理系统的根本原因。ARIMA模型的原始应用提供了如何通过实施各种方法来促进有效的安全管理系统来影响事故和死亡人数。ARIMA模型需要预测元素随时间的随机模式的数据序列,并产生一个方程。在模型识别之后,它可以根据它的现有值和未来值来预测事件的未来模式。在这项研究工作中,从巴基斯坦矿山和矿物监察局、矿工联合会和报纸收集了2006-2019年期间的事故数据,以评估长期预测。结果表明,ARIMA(2,1,0)模型对于煤矿事故和工人死亡都是合适的模型。从2020年到2025年,事故和死亡人数进行了预测。研究结果表明,政策制定者应系统考虑事故和死亡人数增加可能带来的风险,并制定安全有效的工作平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信