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Improving ATM Network Resilience with Machine Learning

Sanaei, Rasoul (2019) Improving ATM Network Resilience with Machine Learning. WAW Machine Learning IV, 06-07 Jun 2019, cologne, Germany.

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Abstract

This presentation is demonstrating how challenging it is to improve the resiliency of European Air Traffic Management (ATM) Network. The importance of available databases is considered along with benefits of Machine Learning to address this challenge. On top of conceptual background and theoritical approach, NetRes snapshots (front-end) is also included. NetRes is the developed tool in Python which contributes to network state identification based on Capacity regulations.

Item URL in elib:https://elib.dlr.de/128253/
Document Type:Conference or Workshop Item (Speech)
Title:Improving ATM Network Resilience with Machine Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Sanaei, RasoulRasoul.Sanaei (at) dlr.deUNSPECIFIED
Date:6 June 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Air Traffic Flow Management, ATFM, Resilience, ATFCM, EATMN, Machine Learning
Event Title:WAW Machine Learning IV
Event Location:cologne, Germany
Event Type:Workshop
Event Dates:06-07 Jun 2019
Organizer:Deutsche Zentrum für Luft- und Raumfahrt (DLR)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Air Traffic Concepts and Operation
Location: Hamburg
Institutes and Institutions:Air Transport Operations > Air Transport Infrastructures & Processes
Deposited By: Sanaei, Rasoul
Deposited On:26 Aug 2019 09:43
Last Modified:26 Aug 2019 09:43

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