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@article{ART002336375},
author={Woo Il Choi and JANG DAE WON and Kim, Yonsoo},
title={A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities},
journal={Crisisonomy},
issn={2466-1198},
year={2018},
volume={14},
number={3},
pages={139-148},
doi={10.14251/crisisonomy.2018.14.3.139}

TY - JOUR
AU - Woo Il Choi
AU - JANG DAE WON
AU - Kim, Yonsoo
TI - A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities
JO - Crisisonomy
PY - 2018
VL - 14
IS - 3
PB - Crisis and Emergency Management: Theory and Praxis
SP - 139
EP - 148
SN - 2466-1198
AB - 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.
KW - ire insurance;DNN;tensorflow;drop-out;ReLU
DO - 10.14251/crisisonomy.2018.14.3.139
ER -

Woo Il Choi, JANG DAE WON and Kim, Yonsoo. (2018). A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities. Crisisonomy, 14(3), 139-148.

Woo Il Choi, JANG DAE WON and Kim, Yonsoo. 2018, "A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities", Crisisonomy, vol.14, no.3 pp.139-148. Available from: doi:10.14251/crisisonomy.2018.14.3.139

Woo Il Choi, JANG DAE WON, Kim, Yonsoo "A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities" Crisisonomy 14.3 pp.139-148 (2018) : 139.

Woo Il Choi, JANG DAE WON, Kim, Yonsoo. A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities. 2018; 14(3), 139-148. Available from: doi:10.14251/crisisonomy.2018.14.3.139

Woo Il Choi, JANG DAE WON and Kim, Yonsoo. "A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities" Crisisonomy 14, no.3 (2018) : 139-148.doi: 10.14251/crisisonomy.2018.14.3.139

Woo Il Choi; JANG DAE WON; Kim, Yonsoo. A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities. Crisisonomy, 14(3), 139-148. doi: 10.14251/crisisonomy.2018.14.3.139

Woo Il Choi; JANG DAE WON; Kim, Yonsoo. A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities. Crisisonomy. 2018; 14(3) 139-148. doi: 10.14251/crisisonomy.2018.14.3.139

Woo Il Choi, JANG DAE WON, Kim, Yonsoo. A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities. 2018; 14(3), 139-148. Available from: doi:10.14251/crisisonomy.2018.14.3.139

Woo Il Choi, JANG DAE WON and Kim, Yonsoo. "A Comparative Study of Shallow Learning with Deep Learning Neural Networks on Fire Accident Prediction for Industrial Facilities" Crisisonomy 14, no.3 (2018) : 139-148.doi: 10.14251/crisisonomy.2018.14.3.139