analysing incidents using data enrichment and machine learning meeting 1... · applying machine...
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Gerard van Es
ICAO First iSTARS User Group (iUG/01)
ICAO Headquarters, Montreal – 17-19 December 2018
Analysing Incidents using Data Enrichment and Machine
Learning
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Background – occurrence data
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Occurrence data
Operators
ATC
Airport
Ground services
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Background – Issues with reported occurrences
Incomplete reports (data fields missing);
Incorrect reports (errors in data fields).
Limits our data analyses!
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How to solve these problems?
Occurrence data
METARs
Radar/ADS-B
Register database
Airport databases
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Example using datamining to code occurrence
category
Random Forest classifier (gave best results out of 5 techniques);
Random Forest builds multiple decision trees and merges them together
to get a more accurate prediction;
Performance on training dataset gave accuracy of 94%;
Performance on complete set gave accuracy of 79% (+48,000
occurrences);
Difficulties with SCF-NP/PP and ADRM & ATC occurrences.
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Occurrence data enrichment
Add information to occurrence data normally not reported, e.g.:
Flown track;
Duration of flight;
Additional weather data (en-route);
Taxi track;
Non-occurrence flights with same details as for occurrences.
Extends analyses possibilities
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Why look at non-occurrence flights?
Normalising of occurrences (classical approach);
Allows to explore contribution of certain factors to overall risk increase;
Will highlight importance of factors not visible from occurrence data only.
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Example case
Reported go-arounds at an airport;
What is driving the rate of go-arounds at the airport?
Simply plotting number of go-arounds by year etc. will not be very
meaningful...
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Typical reasons for a go-around
How important are these factors?
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Relation between crosswind and go-around rate
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Example Machine Learning / Data Mining methods
* Credits: Innaxis Keynote, EUROCONTROL TIM/ART workshop ML and AI, 24-25 sept 2018
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Text mining of
occurrence data by
applying t-SNE algorithm
to find patterns
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Occurrence categories
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Applying machine learning to enriched occurrence
data
Will help to predict under which conditions there is higher risk of a
particular occurrence;
For instance, combination of wind, period in time, light conditions and
runway use could increase go-around rate;
Need sufficient amount of occurrence data to build accurate models.
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Machine Learning – important rules
You need sufficient and reliable data!
Define your outcome!
It is not a push button exercise – You must understand aviation!
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NLR Amsterdam
Anthony Fokkerweg 2
1059 CM Amsterdam
p ) +31 88 511 31 13 f ) +31 88 511 32 10
e ) [email protected] i ) www.nlr.nl
NLR Marknesse
Voorsterweg 31
8316 PR Marknesse
p ) +31 88 511 44 44 f ) +31 88 511 42 10
e ) [email protected] i ) www.nlr.nl
Fully engaged Netherlands Aerospace Centre