实现更安全的采矿环境:深入审查事故预测模型

IF 1.827 Q2 Earth and Planetary Sciences Arabian Journal of Geosciences Pub Date : 2024-10-08 DOI:10.1007/s12517-024-12090-4
Kausar Sultan Shah, Hafeez Ur Rehman, Niaz Muhammad Shahani, Barkat Ullah, Naeem Abbas, Muhammad Junaid, Mohd Hazizan bin Mohd Hashim
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引用次数: 0

摘要

采矿业在许多国家都具有重要的经济意义,但由于其固有的特点,它也被认为是最危险的行业之一。在全球范围内,采矿事故是造成人员伤亡的主要原因。因此,这一问题成为研究领域的重点,促使人们研究用于分析和预测采矿事故的复杂算法和模型。这些努力的主要目的是确定导致此类事故的关键因素。对采矿事故预测的研究旨在开发能够提供更安全的工作环境的技术,并最终为保护人类生命做出贡献。本研究的主要目的是深入概述采矿事故预测领域的最新发展。这一全面概述涵盖各种方法,包括时间序列分析方法、统计方法、数据科学技术、机器学习和深度学习算法。此外,本文还对常用于预测采矿事故的主要数据源进行了全面分析和研究。为了全面分析材料,本文概述并比较了用于预测采矿事故的多种算法。分析包括各种算法的详尽汇编和比较评估。此外,还根据分析数据的特点评估了这些算法的适用性。获得的结果及其解释和分析的简易性也同样受到审查。作者指出,将两种或两种以上的分析程序结合起来,可以取得最有利的结果,从而加强对给定结果的审查。在即将面临的挖掘、预测问题中,通过纳入异构数据源(如地理数据、视频、录音、文本内容、情感和情商),正在扩大所建议的模型和预测的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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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来源期刊
Arabian Journal of Geosciences
Arabian Journal of Geosciences GEOSCIENCES, MULTIDISCIPLINARY-
自引率
0.00%
发文量
1587
审稿时长
6.7 months
期刊介绍: The Arabian Journal of Geosciences is the official journal of the Saudi Society for Geosciences and publishes peer-reviewed original and review articles on the entire range of Earth Science themes, focused on, but not limited to, those that have regional significance to the Middle East and the Euro-Mediterranean Zone. Key topics therefore include; geology, hydrogeology, earth system science, petroleum sciences, geophysics, seismology and crustal structures, tectonics, sedimentology, palaeontology, metamorphic and igneous petrology, natural hazards, environmental sciences and sustainable development, geoarchaeology, geomorphology, paleo-environment studies, oceanography, atmospheric sciences, GIS and remote sensing, geodesy, mineralogy, volcanology, geochemistry and metallogenesis.
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