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dc.contributor.authorAk, Muhammet Fatih
dc.contributor.editorAl-Turjman, Fadi
dc.identifier.citationAk, M. F. (2019). A neuro-fuzzy-based multi-criteria risk evaluation approach: a case study of underground mining. Al-Turjman, F. (Ed.), Artificial Intelligence in IoT, (pp. 167-205). Berlin: Springer.en_US
dc.description.abstractUnderground mining is considered as one of the most hazard-prone industries, and serious work-related fatalities have arisen as a consequence of processes related to it; this chapter deals with occupational hazards and related risk factors. Artificial neural network-based risk assessment approach in underground copper and zinc mine case study is proposed. Occupational health and safety (OHS) history dates back to ancient human history ever. Mankind date was obliged to do business in order to sustain life. OHS studies aim to increase the safety standard with reducing risk level in an acceptable degree. Safe workplaces with respect to OHS increase health, safety, and welfare standards of whole workers. Throughout the world major hazards categorized as physical, chemical, biological, psychosocial, and ergonomic risks can be observed. Although technological developments provide rapid growth in almost all industries, it can be observed that there is a lack of attention being paid and advanced occupational safety practices in the mining industry. A case study is carried out in one of the largest underground mining companies using neuro-fuzzy approach. Neuro-fuzzy logic-based risk assessment study supplies opportunity to provide more adequate decision-making process and gives meaningful classifications of hazard. Neuro-fuzzy approach is a combination of advantages of artificial neural networks and fuzzy logic. It gives more appropriate and comprehensive risk assessment in OHS. After all the neuro-fuzzy approach is applied for classification of risk types in each department of the copper and zinc mine, the necessary control measures for each department and for a whole system are presented. In the study, adaptive neuro-fuzzy inference system (ANFIS)-focused model is applied to the copper and zinc mine risk analysis problem based on three-step neuro-fuzzy approach. Improvements are shown on the study to show the efficiency and flexibility of the method. The main target by integrating the neuro-fuzzy logic application into the risk analysis is to obtain a more effective risk assessment and getting better results than the conventional models used. In conclusion, besides its theoretical contribution, obtained results of this study contribute toward improving occupational safety levels of copper and zinc mine with more comprehensive risk assessment process.en_US
dc.description.sponsorshipNo sponsoren_US
dc.subjectMulti-criteria decision-makingen_US
dc.subjectRisk assessmenten_US
dc.subjectOccupational safety and healthen_US
dc.subjectAnalytic hierarchy processen_US
dc.subjectL-matrix ANFISen_US
dc.subjectÇok kriterli karar vermetr_TR
dc.subjectRisk değerlendirmesitr_TR
dc.subjectİş sağlığı ve güvenliğitr_TR
dc.subjectAnalitik hiyerarşi sürecitr_TR
dc.subjectL-matris ANFIStr_TR
dc.titleA neuro-fuzzy-based multi-criteria risk evaluation approach: a case study of underground miningen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.contributor.orcid0000-0003-4342-296X [Ak, Muhammet Fatih]
dc.contributor.abuauthorAk, Muhammet Fatih
dc.contributor.yokid279243 [Ak, Muhammet Fatih]

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