Short‐term multivariate airworthiness forecasting based on decomposition and deep prediction models

IF 3.4 3区 经济学 Q1 ECONOMICS
Ali Tatli, Tansu Filik, Erdogan Bocu, Hikmet Tahir Karakoc
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引用次数: 0

Abstract

This study introduces a model for predicting airworthiness in terms of meteorology information within the viewpoint of not only formal regulations but also informal rules based on acquired indicators from flight training organization experience (AIs‐FTOE). The case study is carried out in the Hasan Polatkan Airport which is used by the Department of Flight Training of Eskişehir Technical University (ESTU‐P), which is also recognized as a flight training organization. Within the study, the constraints (derived from regulations and AIs‐FTOE) and the data set used in models are explained. Also, the models are introduced based on the gated recurrent unit (GRU) and long short‐term memory (LSTM) with the use of empirical mode decomposition (EMD) and variational mode decomposition (VMD). Finally, a model‐selective mechanism (MSM) is proposed to use the models in common. The findings show that the models presented in the study produce successful results that can be used in flight training organization's (FTO) planning studies. The MSM uses GRU and LSTM together with decomposition techniques to provide more advanced prediction capabilities. When the literature is examined, it is observed that although meteorological conditions are of vital importance in the efficiency of FTOs, there are not enough studies on airworthiness based on meteorology. So, a model that will assist in scheduling plans is presented for FTOs. Airworthiness analysis of forecasting can provide a comprehensive reference to support planning efficiency in FTOs. To the authors' knowledge, this study will be the first in the literature on airworthiness that presents the MSM using a hybrid deep learning algorithm and decomposition of time series models in concurrent.
基于分解和深度预测模型的短期多变量适航性预测
本研究从气象信息的角度引入了一个适航性预测模型,该模型不仅基于正式法规,还基于基于飞行训练组织经验(AIs-FTOE)所获得指标的非正式规则。案例研究在埃斯基谢希尔技术大学(ESTU-P)飞行培训部使用的哈桑-波拉特坎机场进行,该机场也是公认的飞行培训机构。本研究对模型中使用的约束条件(源自法规和 AIs-FTOE)和数据集进行了解释。此外,还介绍了基于门控循环单元(GRU)和长短期记忆(LSTM)的模型,并使用了经验模式分解(EMD)和变异模式分解(VMD)。最后,还提出了一种模型选择机制(MSM),以使用共同的模型。研究结果表明,研究中提出的模型能产生成功的结果,可用于飞行训练组织(FTO)的规划研究。MSM 使用 GRU 和 LSTM 以及分解技术来提供更先进的预测能力。在研究文献时,我们发现虽然气象条件对 FTO 的效率至关重要,但基于气象学的适航性研究还不够多。因此,我们提出了一个有助于为 FTO 制定排班计划的模型。适航性预报分析可为支持 FTO 计划效率提供全面参考。据作者所知,这项研究将是适航性文献中首次使用混合深度学习算法和并发时间序列模型分解提出 MSM。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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