Intrinsic Explainable Self-Enforcing Networks Using the ICON-D2-Ensemble Prediction System for Runway Configurations

Abstract

Weather forecasts are indispensable for the decision on the direction of operation of a runway system. Since the forecasts contain uncertainties, additional challenges arise for runway configuration management (RCM). With developments in machine learning, numerous models have been developed to improve forecasts and assist management. In this contribution, an intrinsically explainable Self-Enforcing Network (SEN) is presented as a decision support system for the RCM at Frankfurt Airport.

Keywords

• Self-Enforcing Networks (SEN)
• Explainable AI (xAI)
• ICON-D2-EPS
• runway configurations