Yapay Sinir Ağı Kullanılarak Petrol Sektöründe Yaşanan İş Kazalarının İncelenmesi

Abstract

Türkiye’de her yıl birçok sektörde iş kazası yaşanmaktadır. Petrol sektöründe meydana gelen iş kazalarının değerlendirilmesini amaçlayan bu çalışmada yapay sinir ağları kullanılarak kaza tahminlemesi yapılmıştır. Petrol sektöründe faaliyet gösteren bir şirkette 2020-2023 yıllarında meydana gelmiş olan 2210 adet iş kazası verileri kullanılmıştır. Çalışmada; aylık kaza verileri ile yapay sinir ağı modellemesi yapılmıştır. Çalışmada ileri beslemeli ağlardan olan Çok Katmanlı Algılayıcı yapay sinir ağları (ÇKAYSA) ile Radyal Tabanlı Fonksiyon yapay sinir ağları (RTFYSA) kullanılmıştır. Çalışmada verilerin %70’i eğitim verisi diğerleri ise test verisi olarak kullanılmıştır. Analizler sonucunda; ÇKAYSA yönteminde %84.1 doğru sınıflama oranı, RTFYSA yöntemi ile %86.4 doğru sınıflama oranı elde edilmiştir. RTFYSA yönteminin ÇKAYSA yöntemine göre daha başarılı performans gösterdiği söylenebilir. Yöntemlerin iş kazalarının tahmini amacıyla kullanılması önerilmektedir

Keywords

• iş sağlığı ve güvenliği
• iş kazaları
• petrol sektörü
• veri madenciliği
• yapay sinir ağları

Investigation of Work Accidents Occuring in the Oil Industry using Artificial Neural Network

Abstract

Occupational accidents occur in many sectors in Türkiye every year. In this study, which aims to evaluate occupational accidents occurring in the oil sector, accident estimation was made using artificial neural networks. Data on 2210 work accidents that occurred between 2020 and 2023 in a company operating in the oil sector were used. In this study; Artificial neural network modeling was done with monthly accident data. In the study, Multilayer Perceptron Artificial Neural Networks (MLPANN) and Radial Basis Function Artificial Neural Networks (RBFANN), which are feed-forward networks, were used. 70% of the data is divided as training data and 30% as test data. As a result of the analysis; An 84.1% correct classification rate was obtained with the MLPANN method, and an 86.4% correct classification rate was obtained with the RBFANN method. It can be said that the RBFANN method performs more successfully than the MLPANN method. lt is suggested to use the methods in order to estimate the occupational accidents .

Keywords

• occupational health and Safety
• work accidents
• oil industry
• data mining
• artificial neural networks

Thanks

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