Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN" Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 )

The Testing and Examination of Unsteady Aerodynamics in a Sling Load System

Presented at the 4th Annual Undergraduate Research Spring Symposium, April 1, 2009 in Room 321 of the Georgia Tech Student Center.Mentor: Narayanan Komerath, A

Convection indicator for pre-tactical air traffic flow management using neural networks

Convective weather is a large source of disruption for air traffic management operations. Being able to predict thunderstorms the day before operations can help traffic managers plan around weather and improve air traffic flow management operations. In this paper, machine learning is applied on data from satellite storm observations and ensemble numerical weather prediction products to detect convective weather 36 h in advance. The learning task is formulated as a binary classification problem and a neural network is trained to predict the occurrence of storms. The neural network results are used to develop a probabilistic based convection indicator capable of outperforming existing convection indicators found in the literature. Lastly, applications of the neural network based indicator in an air traffic management setting are presented

Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented.QC 20231127</p