{"title":"Time Series Forecasting of Indian Coal Mines Fatal Accidents","authors":"Abinash Mohanty, Devidas S. Nimaje","doi":"10.1134/s1062739123060200","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The present study analyzes the fatal accident occurrences of seventy years from 1951 to 2020 in Indian coal mines. The autoregressive integrated moving average (ARIMA) model, Brown’s double exponential smoothing method, Holt’s double exponential smoothing method, and neural network time series forecasting are used in this research to analyze fatal accidents and forecast future accident incidents. By analyzing various parameters of the applied models, the neural network model was found to be the most appropriate model for the collected data to forecast Indian coal mine accidents as it provides the least root mean squared error (RMSE) (17.62), and mean absolute error (MAE) (13.33) among all models. According to this study, the Neural Network model is the most suitable one to predict Indian coal mine fatality.</p>","PeriodicalId":16358,"journal":{"name":"Journal of Mining Science","volume":"109 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mining Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1134/s1062739123060200","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MINING & MINERAL PROCESSING","Score":null,"Total":0}
引用次数: 0
Abstract
The present study analyzes the fatal accident occurrences of seventy years from 1951 to 2020 in Indian coal mines. The autoregressive integrated moving average (ARIMA) model, Brown’s double exponential smoothing method, Holt’s double exponential smoothing method, and neural network time series forecasting are used in this research to analyze fatal accidents and forecast future accident incidents. By analyzing various parameters of the applied models, the neural network model was found to be the most appropriate model for the collected data to forecast Indian coal mine accidents as it provides the least root mean squared error (RMSE) (17.62), and mean absolute error (MAE) (13.33) among all models. According to this study, the Neural Network model is the most suitable one to predict Indian coal mine fatality.
期刊介绍：
The Journal reflects the current trends of development in fundamental and applied mining sciences. It publishes original articles on geomechanics and geoinformation science, investigation of relationships between global geodynamic processes and man-induced disasters, physical and mathematical modeling of rheological and wave processes in multiphase structural geological media, rock failure, analysis and synthesis of mechanisms, automatic machines, and robots, science of mining machines, creation of resource-saving and ecologically safe technologies of mineral mining, mine aerology and mine thermal physics, coal seam degassing, mechanisms for origination of spontaneous fires and methods for their extinction, mineral dressing, and bowel exploitation.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
