Skip to main navigation
Skip to search
Skip to main content
PolyU Scholars Hub Home
Help & FAQ
Home
Researchers
Units
Research output
Prizes
Activities
Press/Media
Student theses
Search by expertise, name or affiliation
Ensemble machine learning models for aviation incident risk prediction
Xiaoge Zhang
, Sankaran Mahadevan
Research output
:
Journal article publication
›
Journal article
›
Academic research
›
peer-review
180
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Ensemble machine learning models for aviation incident risk prediction'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Hybrid Model
100%
Risk Level
100%
Risk Prediction
100%
Incidence Risk
100%
Aviation Accidents
100%
Ensemble Machine Learning Model
100%
High Risk
66%
Media Risk
66%
Ensemble of Deep Neural Networks
66%
Event Consequences
66%
System Safety
33%
Statistical Test
33%
Four-step
33%
Predictive Models
33%
Support Vector Machine
33%
Low Risk
33%
Event-based
33%
Support Vector Machine Model
33%
Fusion Rule
33%
10-fold Cross Validation
33%
Spectacular
33%
Machine Learning Models
33%
Incident Reporting
33%
Air Transportation System
33%
Contextual Features
33%
Event-level
33%
Active Safety
33%
Air Demand
33%
Cross-validation Test
33%
Hazardous Events
33%
Event Outcomes
33%
Probabilistic Decision Tree
33%
Proposed Methodology
33%
Result Prediction
33%
Text Formats
33%
Aviation Safety Reporting System
33%
Engineering
Risk Prediction
100%
Hybrid Model
100%
Learning System
100%
Risk Level
66%
Deep Neural Network
66%
Support Vector Machine
66%
Marine Safety
33%
System Safety
33%
Fusion Rule
33%
Level Event
33%
Outcome Event
33%
Air Traffic
33%
Reporting System
33%
Air Transportation
33%
Computer Science
Deep Neural Network
100%
Support Vector Machine
100%
Machine Learning
100%
Learning System
100%
Fold Cross Validation
50%
Traffic Demand
50%
Individual Model
50%
Decision Tree
50%
Hazardous Event
50%
Predictive Model
50%
Incident Report
50%
Mathematics
Support Vector Machine
100%
Deep Neural Network
100%
Risk Level
100%
Predictive Model
50%
Wide Variety
50%
Cross-Validation
50%
Decision Tree
50%
Statistical Test
50%
Psychology
Neural Network
100%
Learning Model
100%
High-Risk Population
50%
Cross-Validation
50%
Chemical Engineering
Learning System
100%
Deep Neural Network
100%
Support Vector Machine
100%