Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques
https://orcid.org/http://orcid.org/0000-0002-1524-5874
Risk management focusses on the identification of uncertainties and its impacts associated with various functional activities carried out to achieve various mandates, goals and objectives of the company. To assess the risk level (as ‘very high’, ‘high’, ‘medium’ or ‘low’), consequences and likelihood analysis are to be done based on the judgmental knowledge and experiences of the participants. The traditional methods of risk classification are time consuming and laborious if the inputs are voluminous. In this study, the hazards occurring in different sections of underground mining have been categorized, and associated risks have been predicted using different machine learning modelling techniques, namely KNN, SVM, logistic regression and decision tree by keeping the prescribed guidelines of the DGMS intact and using it as the basic building blocks to model the machine learning classification models.
Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques
https://orcid.org/http://orcid.org/0000-0002-1524-5874
Risk management focusses on the identification of uncertainties and its impacts associated with various functional activities carried out to achieve various mandates, goals and objectives of the company. To assess the risk level (as ‘very high’, ‘high’, ‘medium’ or ‘low’), consequences and likelihood analysis are to be done based on the judgmental knowledge and experiences of the participants. The traditional methods of risk classification are time consuming and laborious if the inputs are voluminous. In this study, the hazards occurring in different sections of underground mining have been categorized, and associated risks have been predicted using different machine learning modelling techniques, namely KNN, SVM, logistic regression and decision tree by keeping the prescribed guidelines of the DGMS intact and using it as the basic building blocks to model the machine learning classification models.
Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques
https://orcid.org/http://orcid.org/0000-0002-1524-5874
Risk management focusses on the identification of uncertainties and its impacts associated with various functional activities carried out to achieve various mandates, goals and objectives of the company. To assess the risk level (as ‘very high’, ‘high’, ‘medium’ or ‘low’), consequences and likelihood analysis are to be done based on the judgmental knowledge and experiences of the participants. The traditional methods of risk classification are time consuming and laborious if the inputs are voluminous. In this study, the hazards occurring in different sections of underground mining have been categorized, and associated risks have been predicted using different machine learning modelling techniques, namely KNN, SVM, logistic regression and decision tree by keeping the prescribed guidelines of the DGMS intact and using it as the basic building blocks to model the machine learning classification models.
Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques
J. Inst. Eng. India Ser. D
Tripathy, D. P. (Autor:in) / Parida, Satyajeet (Autor:in) / Khandu, Leki (Autor:in)
Journal of The Institution of Engineers (India): Series D ; 102 ; 495-504
01.12.2021
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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