波兰煤矿事故预测模型

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR) Pub Date : 2022-08-22 DOI:10.1109/MMAR55195.2022.9874303

Marta Kraszewska, Nataliia Kashpruk, J. Baranowski, M. Kapusta

{"title":"波兰煤矿事故预测模型","authors":"Marta Kraszewska, Nataliia Kashpruk, J. Baranowski, M. Kapusta","doi":"10.1109/MMAR55195.2022.9874303","DOIUrl":null,"url":null,"abstract":"In paper, forecasting models using exponential smoothing for Polish coal mining safety accidents are presented. Prior to this, data is analyzed and approach for building forecasting models in Tableau is described in details. Three forecasting models are revealed, respectively for all accidents in coal mines, fatal accidents and employment. Received results are promising and confidence intervals cover the predictions well. Improved forecast accuracy with presented models might provide coal mine enterprises more precise data, supporting safety management in those organizations.","PeriodicalId":169528,"journal":{"name":"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Forecasting models for Polish coal mining accidents\",\"authors\":\"Marta Kraszewska, Nataliia Kashpruk, J. Baranowski, M. Kapusta\",\"doi\":\"10.1109/MMAR55195.2022.9874303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In paper, forecasting models using exponential smoothing for Polish coal mining safety accidents are presented. Prior to this, data is analyzed and approach for building forecasting models in Tableau is described in details. Three forecasting models are revealed, respectively for all accidents in coal mines, fatal accidents and employment. Received results are promising and confidence intervals cover the predictions well. Improved forecast accuracy with presented models might provide coal mine enterprises more precise data, supporting safety management in those organizations.\",\"PeriodicalId\":169528,\"journal\":{\"name\":\"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"volume\":\"199 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMAR55195.2022.9874303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR55195.2022.9874303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

本文建立了波兰煤矿安全事故的指数平滑预测模型。在此之前，对数据进行了分析，并详细介绍了在Tableau中建立预测模型的方法。建立了煤矿全事故预测模型、致命事故预测模型和就业预测模型。收到的结果是有希望的，置信区间很好地覆盖了预测。提出的模型提高了预测精度，可以为煤矿企业提供更精确的数据，支持煤矿企业的安全管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Forecasting models for Polish coal mining accidents

In paper, forecasting models using exponential smoothing for Polish coal mining safety accidents are presented. Prior to this, data is analyzed and approach for building forecasting models in Tableau is described in details. Three forecasting models are revealed, respectively for all accidents in coal mines, fatal accidents and employment. Received results are promising and confidence intervals cover the predictions well. Improved forecast accuracy with presented models might provide coal mine enterprises more precise data, supporting safety management in those organizations.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)

自引率

0.00%

发文量