Natural Language Processing for the identification of Human factors in aviation accidents causes: An application to the SHEL methodology

Perboli, Guido; Gajetti, Marco; Fedorov, Stanislav; Giudice, Simona Lo

doi:10.1016/j.eswa.2021.115694

Cited by 31 publications

(14 citation statements)

References 12 publications

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“…In addition to the selection of prediction methods, the selection of input variables affects the predicted effect. The classic accident inducement identification model is represented by the SHEL [ 10 ] model, REASON [ 11 ], HFACS [ 12 ] (human factors analysis classification system), OHFAM [ 13 ] (occurrence human factors analysis model) and 24 model [ 14 ]. The expansion of the inducement index is theoretically conducive to obtaining a more accurate causality description but brings the curse of dimensionality accordingly: the higher the dimensions of the input set are, the efficiency of safety prediction will be greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

Research on Aviation Safety Prediction Based on Variable Selection and LSTM

Hang

Guo

Zhang

et al. 2022

Sensors

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Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root-mean-square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness.

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Section: Introductionmentioning

confidence: 99%

Research on Aviation Safety Prediction Based on Variable Selection and LSTM

Hang

Guo

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…The gray entropy correlation degree of each factor of the gray entropy correlation degree is obtained from the equation [ 18 ] ( Fig. 3 ).…”

Section: Resultsmentioning

confidence: 99%

“… Step 5: Calculate the gray entropy correlation degree using equations [ 16 ]-(17), obtain the average correlation coefficient between the evaluation index to be analyzed and the evaluation object, and then obtain the original correlation degree. Reuse equation [ 18 ] and include the entropy weight of the evaluation index to get the final degree of relevance.

…”

Section: Methodsmentioning

confidence: 99%

“…Ni et al (2019) made civil aviation safety evaluation based on deep belief network and principal component analysis. Some scholars also use methods such as the establishment of structural equations, bayesian networks, machine learning, and bowtie models for aircraft fault safety diagnosis or safety assessment applications [ [16] , [17] , [18] , [19] , [20] ]. Many scholars have carried out a series of studies on new energy aircraft, human factors and text mining [ [21] , [22] , [23] ], found safety voice and safety listening during aviation accidents: cockpit voice recordings reveal that speaking-up to power is not enough [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Identification of flight accidents causative factors base on SHELLO and improved entropy gray correlation method

Chen

Sun

Wang

et al. 2023

Heliyon

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“…In addition, it is difficult to monitor effectively the behaviors of humans involved in the shipyard operation. Although the study for human-related factors involved in shipyard operations is still underway, similar studies in other industries have been well developed, such as maritime accidents analysis [ 28 ], aviation accidents investigation [ 29 ], risk evaluation in process industry [ 30 ] and coal mine accidents analysis (Liu et al, 2018). According to the review by Qiao et al [ 31 ], the human-related factor analysis models can be divided into four types, in which various technologies, quantitative or qualitative, are involved.…”

Section: Introductionmentioning

confidence: 99%

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

Liu

Qiao

et al. 2021

IJERPH

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The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

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Natural Language Processing for the identification of Human factors in aviation accidents causes: An application to the SHEL methodology

Cited by 31 publications

References 12 publications

Research on Aviation Safety Prediction Based on Variable Selection and LSTM

Research on Aviation Safety Prediction Based on Variable Selection and LSTM

Identification of flight accidents causative factors base on SHELLO and improved entropy gray correlation method

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

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