Understanding Aviation Sustainability with XAI
Presentation Type
Long presentation (faculty/staff) 15-20 minutes
In Person or Zoom Presentation
In-Person
Campus
Worldwide
Status
Faculty
Faculty/Staff Department
Decision Sciences
Student Year and Major
N/A
Invited Speaker Institution/Department
ERAU WW, COB, Decision Sciences and Analytics
Presentation Description/Abstract
This project uses open aviation operations datasets to build machine-learning and explainable AI (XAI) models that forecast short- and medium-term sustainability outcomes. Target variables include gate-to-gate CO₂ estimates, flight efficiency (horizontal/vertical), surface inefficiency (e.g., additional taxi time), and delay-related indicators at network, state, airport, and service-provider levels. ML and XAI algorithm are trained with rolling-origin validation; performance is reported via SHAP values, partial-dependence plots, and counterfactual analyses provide transparent driver attribution and reveal leverage points—such as continuous climb/descent procedures, pushback control, and demand-capacity alignment. The outcome is a practical, interpretable toolkit that (i) predicts sustainability metrics with actionable confidence intervals, (ii) ranks interventions by marginal emissions-reduction potential, and (iii) supports data-driven prioritization of operational improvements across the aviation network.
Keywords
Aviation, sustainiblity, performace metrics, airlines, states
Understanding Aviation Sustainability with XAI
This project uses open aviation operations datasets to build machine-learning and explainable AI (XAI) models that forecast short- and medium-term sustainability outcomes. Target variables include gate-to-gate CO₂ estimates, flight efficiency (horizontal/vertical), surface inefficiency (e.g., additional taxi time), and delay-related indicators at network, state, airport, and service-provider levels. ML and XAI algorithm are trained with rolling-origin validation; performance is reported via SHAP values, partial-dependence plots, and counterfactual analyses provide transparent driver attribution and reveal leverage points—such as continuous climb/descent procedures, pushback control, and demand-capacity alignment. The outcome is a practical, interpretable toolkit that (i) predicts sustainability metrics with actionable confidence intervals, (ii) ranks interventions by marginal emissions-reduction potential, and (iii) supports data-driven prioritization of operational improvements across the aviation network.