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A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities

  • Crisisonomy
  • Abbr : KRCEM
  • 2018, 14(3), pp.139-148
  • DOI : 10.14251/crisisonomy.2018.14.3.139
  • Publisher : Crisis and Emergency Management: Theory and Praxis
  • Research Area : Social Science > Public Policy > Public Policy in general
  • Received : November 27, 2017
  • Accepted : February 24, 2018
  • Published : March 31, 2018

Woo Il Choi 1 JANG DAE WON 2 Kim, Yonsoo 3

1LIG시스템
2(주)LIG시스템
3(주)엘아이지시스템

Accredited

ABSTRACT

The shallow learning neural network (SNN) has some limitations in the assessment of fire risk of industrial facilities due to its inherent problems such as over-fitting and gradient vanishing. However, in recent years, it has become possible to build a deep learning neural network (DNN) consisting of multiple hidden layers and to make learning algorithms more sophisticated, which allows for the use of a fire risk assessment tool in the fire insurance. In this paper, prediction performances between SNN and DNN are compared under various conditions using Google's Tensorflow. As a result, most SNN problems are solved through the drop-out method and ReLU activation function in DNN, and the learning performance of DNN with a maximum TS value of 0.76 is confirmed to be 58% higher than that of SNN. Nevertheless, in order to improve the utilization of fire insurance as a risk management tool, a systematic and large amount of learning data should be secured.

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