Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151132
Title: ANFIS model for assessing near-miss risk during tanker shipping voyages
Authors: Zhou, Qingji
Wong, Yiik Diew
Loh, Hui Shan
Yuen, Kum Fai
Keywords: Engineering::Environmental engineering
Issue Date: 2019
Source: Zhou, Q., Wong, Y. D., Loh, H. S. & Yuen, K. F. (2019). ANFIS model for assessing near-miss risk during tanker shipping voyages. Maritime Policy and Management, 46(4), 377-393. https://dx.doi.org/10.1080/03088839.2019.1569765
Journal: Maritime Policy and Management
Abstract: Adaptive neuro-fuzzy inference system (ANFIS) was applied to predict the risk of near-miss incidents during tanker shipping voyages. Firstly, near-miss incidents recorded by a global tanker shipping management company were analysed. Four variables—type of operation, vessel’s location, on-board position, and harm potential were selected to train and predict the risk levels of near-miss incidents. The selected variables were found to be correlated with the observed frequency at three risk levels, namely low, medium and high. Gravity factor (GF) was calculated using the frequency of the categories in each variable and their associated risk levels. The calculated GF values and the risk levels of near-miss incidents were used as input values in the ANFIS model. Triangular, Trapezoidal and Gaussian membership functions were used. Subsequently, fuzzy logical theory and artificial neural networks were applied to train the data. Causal factors in terms of direct contributory factors, indirect contributory factors and root contributory factors to the near-miss incidents were analysed. Risk control measures were also proposed to improve safety during tanker shipping.
URI: https://hdl.handle.net/10356/151132
ISSN: 0308-8839
DOI: 10.1080/03088839.2019.1569765
Rights: © 2019 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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