A system for airport weather forecasting based on circular regression trees

This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously modelled and observed data. This is based on a new machine learning methodology that allows circular variables to be naturally incorporated into regression trees, producing more accurate results than linear and previous circular regression tree methodologies. The software has been made publicly available as a Python package, which contains all the necessary tools to extract historical NWP and observed weather data and to generate forecasts for different weather variables for any airport in the world. Several examples are presented where the results of the proposed model significantly improve those produced by NWP and also by previous regression tree models.TIN2016-78365-R, IT609-1

Application of machine learning techniques to weather forecasting

84 p.El pronóstico del tiempo es, incluso hoy en día, una actividad realizada principalmente por humanos. Si bien las simulaciones por computadora desempeñan un papel importante en el modelado del estado y la evolución de la atmósfera, faltan metodologías para automatizar la interpretación de la información generada por estos modelos. Esta tesis doctoral explora el uso de metodologías de aprendizaje automático para resolver problemas específicos en meteorología y haciendo especial énfasis en la exploración de metodologías para mejorar la precisión de los modelos numéricos de predicción del tiempo. El trabajo presentado en este manuscrito comprende dos enfoques diferentes a la aplicación de algoritmos de aprendizaje automático a problemas de predicción meteorológica. En la primera parte, las metodologías clásicas, como la regresión multivariada no paramétrica y los árboles binarios, se utilizan para realizar regresiones en datos meteorológicos. Esta primera parte, está centrada particularmente en el pronóstico del viento, cuya naturaleza circular crea desafíos interesantes para los algoritmos clásicos de aprendizaje automático. La segunda parte de esta tesis explora el análisis de los datos meteorológicos como un problema de predicción estructurado genérico utilizando redes neuronales profundas. Las redes neuronales, como las redes convolucionales y recurrentes, proporcionan un método para capturar la estructura espacial y temporal inherente en los modelos de predicción del tiempo. Esta parte explora el potencial de las redes neuronales convolucionales profundas para resolver problemas difíciles en meteorología, como el modelado de la precipitación a partir de campos de modelos numéricos básicos. La investigación que sustenta esta tesis sirve como un ejemplo de cómo la colaboración entre las comunidades de aprendizaje automático y meteorología puede resultar mutuamente beneficiosa y conducir a avances en ambas disciplinas. Los modelos de pronóstico del tiempo y los datos de observación representan ejemplos únicos de conjuntos de datos grandes (petabytes), estructurados y de alta calidad, que la comunidad de aprendizaje automático exige para desarrollar la próxima generación de algoritmos escalables

Application of machine learning techniques to weather forecasting

Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the interpretation of the information generated by these models. This doctoral thesis explores the use of machine learning methodologies to solve specific problems in meteorology and particularly focuses on the exploration of methodologies to improve the accuracy of numerical weather prediction models using machine learning. The work presented in this manuscript contains two different approaches using machine learning. In the first part, classical methodologies, such as multivariate non-parametric regression and binary trees are explored to perform regression on meteorological data. In this first part, we particularly focus on forecasting wind, where the circular nature of this variable opens interesting challenges for classic machine learning algorithms and techniques. The second part of this thesis, explores the analysis of weather data as a generic structured prediction problem using deep neural networks. Neural networks, such as convolutional and recurrent networks provide a method for capturing the spatial and temporal structure inherent in weather prediction models. This part explores the potential of deep convolutional neural networks in solving difficult problems in meteorology, such as modelling precipitation from basic numerical model fields. The research performed during the completion of this thesis demonstrates that collaboration between the machine learning and meteorology research communities is mutually beneficial and leads to advances in both disciplines. Weather forecasting models and observational data represent unique examples of large (petabytes), structured and high-quality data sets, that the machine learning community demands for developing the next generation of scalable algorithms

Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting

While circular data occur in a wide range of scientific fields, the methodology for distributional modeling and probabilistic forecasting of circular response variables is rather limited. Most of the existing methods are built on the framework of generalized linear and additive models, which are often challenging to optimize and to interpret. Therefore, we suggest circular regression trees and random forests as an intuitive alternative approach that is relatively easy to fit. Building on previous ideas for trees modeling circular means, we suggest a distributional approach for both trees and forests yielding probabilistic forecasts based on the von Mises distribution. The resulting tree-based models simplify the estimation process by using the available covariates for partitioning the data into sufficiently homogeneous subgroups so that a simple von Mises distribution without further covariates can be fitted to the circular response in each subgroup. These circular regression trees are straightforward to interpret, can capture nonlinear effects and interactions, and automatically select the relevant covariates that are associated with either location and/or scale changes in the von Mises distribution. Combining an ensemble of circular regression trees to a circular regression forest yields a local adaptive likelihood estimator for the von Mises distribution that can regularize and smooth the covariate effects. The new methods are evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability

A multi-level predictive methodology for terminal area air traffic flow

Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As the demand for air travel expands, airport departure/arrival demand almost reaches its capacity. In consequence, the level of delays increases since the system capacity cannot manage the increased demand. With this trend, the national airspace system (NAS) will be saturated, and the congestion at the airport will become even more severe. As a result of congestion, a considerable number of flights experience delays. According to the Bureau of Transportation Statistics (BTS), over 1 million flights are operated in a year, and about twenty percent of all scheduled commercial flights are delayed more than 15 minutes. These delays cost billions of dollars annually for airlines, passengers, and the US economy. Therefore, this study seeks to find out why the delays occur and to analyze patterns in which the delays occurred. Analysis of airport operations generally falls into a macro or micro perspective. At the macro point of view, very few details are considered, and delays are aggregated at the airport level. Especially, shortfalls in airport capacity and a capacity-demand imbalance are the primary causes of delays in this respect. In the micro perspective, each aircraft is modeled individually, and the causes of delays are reproduced as precisely as possible. Micro reasons for air traffic delays include inclement weather, mechanics problems, operation issues. In this regard, this research proposes a methodology that can efficiently and practically predict macro and micro-level air traffic flow in the terminal area. For a macro-level analysis of delays, artificial neural networks models are proposed to predict the hourly airport capacity. Multi-layer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM) are trained with historical weather and airport capacity data of Hartsfield-Jackson Atlanta airport (ATL). In the performance evaluation, the models have presented decent predictive performance and successfully predicted the test data as well as the training data. On the other hand, Random Forests and AdaBoost are implemented in the micro-level modeling of the air traffic. The micro-level models trained with on-time flight performance data and corresponding weather data focus on a classification of the individual flight delays. The model provides interpretability and imbalanced data handling while the accuracy is as good as the existing methods. Lastly, the predictive model for individual flight delays is refined using the cost-proportionate rejection sampling (costing) method. Along with the integration of the costing method, general machine learning algorithms have been converted to cost-sensitive classifiers. The cost-sensitive classifiers were able to account for asymmetric misclassification costs without losing their diagnostic functionality as binary classifiers. This study presents a data-driven approach to air traffic flow management that can effectively utilize air traffic data accumulated over decades. Through data analysis from the macro and micro perspective, an integrated methodology for terminal air traffic flow prediction is provided. An accurate prediction of the airport capacity and individual flight delays will assist stakeholders in taking more informed decisions.Ph.D

Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather

Growth in air traffic demand in the United States has led to an increase in ground delays at major airports in the nation. Ground delays, including taxi time delays, directly impacts the block time and block fuel for flights which affects the airlines operationally and financially. Additionally, runway configuration selection at an airport significantly impacts the airport capacity, throughput, and delays as it is vital in directing the flow of air traffic in and out of an airport. Runway configuration selection is based on interrelated factors, including weather variables such as wind and visibility, airport facilities such as instrument approach procedures for runways, noise abatement procedures, arrival and departure demand, and coordination of ATC with neighboring airport facilities. The research problem of this study investigated whether runway configuration selection and taxi out times at airports can be predicted with hourly surface weather observations. This study utilized two sequence-to-sequence Deep Learning architectures, LSTM encoderdecoder and Transformer, to predict taxi out times and runway configuration selection for airports in MCO and JFK. An input sequence of 12 hours was used, which included surface weather data and hourly departures and arrivals. The output sequence was set to 6 hours, consisting of taxi out times for the regression models and runway configuration selection for the classification models. For the taxi out times models, the LSTM encoder-decoder model performed better than the Transformer model with the best MSE for output Sequence 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model. For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the Transformer model for the binary classification task at MCO. The LSTM encoder-decoder and Transformer models demonstrated comparable performance for the multiclass classification task at JFK. Out of the six output sequences, Sequence 3 demonstrated the best performance with an accuracy of 80.24 and precision of 0.70 for MCO and an accuracy of 77.26 and precision of 0.76 for JFK. The SHAP analysis demonstrated that the Departure, Dew Point, and Wind Direction variables had the most significant contribution to the predictions of the model

Microburst Recovery For Jet Transport Aircraft: A Comparison Between Constant And Variable Pitch Guidance Trajectories

The purpose of this research was to compare, in a simulator, the safety of a variable pitch strategy with the established constant pitch strategy in transitioning through a microburst during an abort maneuver in the approach to landing phase of flight. In numerous mathematical and computer studies of microburst penetrations, the variable pitch strategy provided a greater recovery altitude than the constant pitch strategy. A Boeing 737 level C aircraft simulator was employed to evaluate these findings in a dynamic environment. Three appropriately qualified subjects piloted 35 flights through a microburst, while computer generated data were collected. Safety , defined as the maximization of the minimum altitude experienced by the aircraft during the recovery phase of the microburst encounter, was statistically greater for the constant pitch maneuver. An improved microburst model and a flight director steering command are recommended for continued studies in a manned simulato