Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network

In the early stage of a fire in a building, the human psychological response becomes an important feature in determining survival. Bayesian Network (BN) model has been one of the preferred choices among researchers in representing uncertain knowledge about the psychological responses in the evacuati...

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Main Author:	Ramli, Nurulhuda
Format:	Thesis
Language:	English
Published:	2020
Subjects:	LC5800-5808 Distance education.
Online Access:	http://eprints.usm.my/53853/1/NURULHUDA%20BINTI%20RAMLI%20-%20TESIS%20cut.pdf
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id	my-usm-ep.53853
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spelling	my-usm-ep.538532022-08-05T07:18:56Z Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network 2020-07 Ramli, Nurulhuda LC5800-5808 Distance education. In the early stage of a fire in a building, the human psychological response becomes an important feature in determining survival. Bayesian Network (BN) model has been one of the preferred choices among researchers in representing uncertain knowledge about the psychological responses in the evacuation system. The BN psychological response models mostly have a literature background of human behaviour in a building on fire. However, the model structure and quantification are almost not involving the opinion of experts in the domain knowledge. Therefore, the goal of this study is to address the uncertainties in the BN model of psychological response during a fire evacuation through expert elicitation. A new conceptual model namely the PRiF (Psychological Response in a Fire) is developed through the social scientific theory and the disaster and fire theory as well as expert opinion approach. For the purpose of model quantification, two phases of data collections involving seven experts from the academic stream and professional fire practitioners are conducted. In the first phase, a new conversion scale for an aided elicitation tool, the Fuzzy Probability Scale is constructed by the experts to elicit the membership functions of the linguistic terms in the scale. The second phase requires the experts to quantify the PRiF model using the Z-number concept, which is able to address the expert’s uncertainty and includes the reliability of their estimates. In doing this, the experts infer their beliefs using the developed conversion scale and provide the confidence of their judgments. 2020-07 Thesis http://eprints.usm.my/53853/ http://eprints.usm.my/53853/1/NURULHUDA%20BINTI%20RAMLI%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Pendidikan Jarak Jauh
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	LC5800-5808 Distance education.
spellingShingle	LC5800-5808 Distance education. Ramli, Nurulhuda Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
description	In the early stage of a fire in a building, the human psychological response becomes an important feature in determining survival. Bayesian Network (BN) model has been one of the preferred choices among researchers in representing uncertain knowledge about the psychological responses in the evacuation system. The BN psychological response models mostly have a literature background of human behaviour in a building on fire. However, the model structure and quantification are almost not involving the opinion of experts in the domain knowledge. Therefore, the goal of this study is to address the uncertainties in the BN model of psychological response during a fire evacuation through expert elicitation. A new conceptual model namely the PRiF (Psychological Response in a Fire) is developed through the social scientific theory and the disaster and fire theory as well as expert opinion approach. For the purpose of model quantification, two phases of data collections involving seven experts from the academic stream and professional fire practitioners are conducted. In the first phase, a new conversion scale for an aided elicitation tool, the Fuzzy Probability Scale is constructed by the experts to elicit the membership functions of the linguistic terms in the scale. The second phase requires the experts to quantify the PRiF model using the Z-number concept, which is able to address the expert’s uncertainty and includes the reliability of their estimates. In doing this, the experts infer their beliefs using the developed conversion scale and provide the confidence of their judgments.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ramli, Nurulhuda
author_facet	Ramli, Nurulhuda
author_sort	Ramli, Nurulhuda
title	Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
title_short	Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
title_full	Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
title_fullStr	Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
title_full_unstemmed	Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network
title_sort	expert elicitation of uncertainty in psychological response in a fire evacuation using bayesian network
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Pendidikan Jarak Jauh
publishDate	2020
url	http://eprints.usm.my/53853/1/NURULHUDA%20BINTI%20RAMLI%20-%20TESIS%20cut.pdf
_version_	1747822261235089408

Expert Elicitation Of Uncertainty In Psychological Response In A Fire Evacuation Using Bayesian Network

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