Time-Series Prediction for Amount of Airworthiness Based on Time-Delay Neural Networks
| Autor: | Sinem Kahvecioglu, Ali Tatli, Hikmet Karakoc |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
020301 aerospace & aeronautics
Computer science Aviation business.industry Time delay neural network Airworthiness Training (meteorology) airworthiness 020206 networking & telecommunications 02 engineering and technology METAR Troposphere tdnn time series prediction 0203 mechanical engineering Aeronautics short-term forecasting 0202 electrical engineering electronic engineering information engineering Visual flight rules lcsh:Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering Flight training business lcsh:TK1-9971 |
| Zdroj: | Elektronika ir Elektrotechnika, Vol 26, Iss 5, Pp 28-32 (2020) |
| ISSN: | 2029-5731 1392-1215 |
| Popis: | Troposphere and the first stratum of the stratosphere are intensely utilized atmosphere layers for the aviation activities. Due to the different performances, capabilities, designs, and equipment of the aerial vehicles, meteorological weather events that occur in the troposphere affect these vehicles at different levels during their aeronautical activities. Although simple aircrafts are more sensitive to the effects of meteorological events, they are especially preferred by flight training organizations (FTOs) in pilotage training when they are considered in terms of maintenance and equipment costs. In cases where inexperienced pilot candidates and simple aircrafts that are more vulnerable to weather events come together, analysis and prediction of meteorological parameters becomes more important in terms of preventing accidents and reducing risks, as well as proper planning for flight and maintenance. The purposes of this study are, first, to derive flight availability time-series for two different types of aircraft according to visual flight rules by using Meteorological Terminal Air Report (METAR), and then to establish and evaluate a prediction model by using Time-Delay Neural Networks (TDNNs). |
| Databáze: | OpenAIRE |
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