# Human Factors as Predictor of Fatalities in Aviation Accidents: A Neural Network Analysis
## 3 Database  
The Aviation Safety Network (ASN) database is a worldwide repository of information on airliner accidents and safety issues kept by a non-profit organization [32]. The data used in this paper correspond to all the 1105 accidents and incidents reported during a period of ten years, from 2007 to 2016. This period was chosen since it is the same as that used in [2; 7]. For each accident or incident, available reports were used to find out which human factors were involved. The list of human factors employed and its numeration, adapted from [3] and its extension in [33] for maintenance activities, is also taken from [2; 7] and is given in Table 2 [2; 7].  
Figure 2: Human Factors Analysis and Classification System (adapted from [2; 30]).  
In addition to the human factors involved in each occurrence, three variables in the metadata of the reports were also obtained:  
* _Type of Occurrence_--This variable captures the nature of what happened. Its possible values are accident, criminal event, incident, and other occurrence.
* _Flight Phase_--Different phases of flight have different risk profiles. For example, take-off and landing are often considered the most critical phases of flight. By including this variable, the study can identify which phases are most susceptible to human factors-related accidents, thereby allowing for targeted interventions. The nine phases considered are, in alphabetical order, approach, en route, initial climb, landing, maneuvering, pushback, standing, take-off, taxi, and unknown.
* _Aircraft Fate_--The aircraft may be repaired or be in such a condition that it is scrapped. This is consequently a binary variable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\#** & **Factor According to HFACS-ME** & **Number of Cases** \\ \hline
1 & Adverse mental state & 73 \\ \hline
2 & Adverse physiological state & 19 \\ \hline
3 & Group resource management & 11 \\ \hline
4 & Dated/Uncertified Equipment & 67 \\ \hline
5 & Decision Error & 62 \\ \hline
6 & Exceptional Violation & 20 \\ \hline
7 & Failed to Fix Known Issue & 12 \\ \hline
8 & Inaccessible & 2 \\ \hline
9 & Inadequate Design & 42 \\ \hline
10 & Inadequate Documentation & 36 \\ \hline
11 & Inadequate supervision & 63 \\ \hline
12 & Inappropriate Operations & 76 \\ \hline
13 & Infraction & 1 \\ \hline
14 & Lighting & 1 \\ \hline
15 & Operational process & 12 \\ \hline
16 & Misperception & 131 \\ \hline
17 & Personal Readiness & 134 \\ \hline
18 & Physical environment & 216 \\ \hline
19 & Physical/mental limitations & 18 \\ \hline
20 & Plan for improper operation & 1 \\ \hline
21 & Resource management & 9 \\ \hline
22 & Routine & 114 \\ \hline
23 & Routine Violation & 39 \\ \hline
24 & Rule & 72 \\ \hline
25 & Skill & 29 \\ \hline
26 & Skill-Based Error & 104 \\ \hline
27 & Supervisory violation & 15 \\ \hline
28 & Technological Environment & 19 \\ \hline
29 & Training & 4 \\ \hline
30 & Problem not fixed & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Human factors, labeled from the contributory causes in the database, with their respective frequency in the database.