Towards safer mining environments: an in-depth review of predictive models for accidents

被引：0
作者：
Kausar Sultan Shah [1 ]
Hafeez Ur Rehman [2 ]
Niaz Muhammad Shahani [3 ]
Barkat Ullah [4 ]
Naeem Abbas [5 ]
Muhammad Junaid [6 ]
Mohd Hazizan bin Mohd Hashim [7 ]
机构：
[1] National University of Science and Technology (NUST),Department of Mining, Engineering and Management Sciences
[2] Balochistan University of Information Technology,School of Mines
[3] China University of Mining and Technology,School of Resources and Safety Engineering
[4] Central South University,Faculty of Land Resource Engineering
[5] Kunming University of Science and Technology,Department of Sustainable Advanced Geomechanical Engineering (SAGE)
[6] Military College of Engineering,School of Materials and Mineral Resources Engineering
[7] (NUST),undefined
[8] Universiti Sains Malaysia,undefined
来源：
Arabian Journal of Geosciences | 2024年 / 17卷 / 11期
关键词：
Mining accidents forecasting; Mining accidents risk prediction; Machine learning; Mining safety; Deep learning;
D O I：
10.1007/s12517-024-12090-4
中图分类号：
学科分类号：
摘要：
The mining industry is of great economic significance in many nations, but it is also considered one of the most dangerous sectors due to its intrinsic characteristics. Mining accidents are a major cause of injuries and fatalities on a global scale. Therefore, this matter receives significant focus within the field of research, prompting the investigation of sophisticated algorithms and models for the analysis and prediction of mining accidents. The primary aim of these endeavors is to ascertain the key components contributing to such mishaps. The study of mining accident forecasting aims to develop technologies that provide a safer working environment and eventually contribute to preserving human lives. The primary aim of this study is to provide an in-depth overview of the latest developments in the field of mining accident prediction. This comprehensive overview spans various methodologies, encompassing time series analysis methods, statistical approaches, data science techniques, machine learning, and deep learning algorithms. Additionally, this article presents a comprehensive analysis and examination of the primary data sources commonly used to predict mining accidents. In order to analyze the material thoroughly, this paper outlines and compares the many algorithms employed to predict mining accidents. The analysis comprises an exhaustive compilation of various algorithms and a comparative evaluation. Moreover, the appropriateness of their suitability is assessed based on the characteristics of the data under analysis. The acquired outcomes and the simplicity of their interpretation and analysis are likewise subject to scrutiny. The authors have stated that the most favorable outcomes are achieved by combining two or more analytic procedures, resulting in an enhanced examination of the given results. Among the upcoming problems in mining, forecasting is expanding the scope of the proposed models and forecasts by incorporating heterogeneous data sources such as geographical data, videos, audio recordings, textual content, sentiment, and emotional intelligence.
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  • [1] Adomako J(2023)Gendered mining landscapes and health implications in Ghana’s artisanal and small-scale gold mining industry J Rural Stud 97 385-394
  • [2] Hausermann H(2023)Comparison of ARIMA, ANN and Hybrid ARIMA-ANN models for time series forecasting information sciences letters An Int J 12 1003-1016
  • [3] Alsuwaylimi AA(2007)Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting J Stat Comput Simul 77 29-53
  • [4] Aslanargun A(2023)Identification and categorization of hazards in the mining industry: a systematic review of the literature Int Rev Appl Sci Eng 15 1-375
  • [5] Mammadov M(2023)Statistical analysis of the severity of occupational accidents in the mining sector J Safety Res 86 364-234
  • [6] Yazici B(2023)Environmental and health risk assessment due to potentially toxic elements in soil near former antimony mine in Western Serbia Land 12 421-247
  • [7] Yolacan S(1994)Time series analysis of coal mine accident experience J Safety Res 25 229-1408
  • [8] Baghaei Naeini(2006)Exponential smoothing model selection for forecasting Int J Forecast 22 239-215
  • [9] Seyedeh Arezoo(2023)The impacts of mining on the food sovereignty and security of Indigenous Peoples and local communities: a global review Sci Total Environ 855 158803-28
  • [10] Badri Adil(2020)Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm Sci Rep 10 9939-14
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