A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

• URL: http://arxiv.org/abs/2107.04010v1
• Date: Thu, 1 Jul 2021 11:01:13 GMT
• Title: A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data
• Authors: Alise Danielle Midtfjord, Riccardo De Bin and Arne Bang Huseby
• Abstract summary: Snow and ice on runway surfaces reduces tire-pavement friction needed for retardation and directional control. XGBoost is used to create a combined runway assessment system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classifcation model to predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Research on Dangerous Flight Weather Prediction based on Machine Learning [4.352963801845065]
  The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather.
  arXiv  Detail & Related papers  (2024-06-18T06:08:15Z)
• Context-Aware Generative Models for Prediction of Aircraft Ground Tracks [0.004807514276707785]
  Trajectory prediction plays an important role in supporting the decision-making of Air Traffic Controllers. Traditional TP methods are deterministic and physics-based, with parameters calibrated using aircraft surveillance data harvested across the world. This work proposes a generative method for lateral TP, using probabilistic machine learning to model the effect of the unknown effect of pilot behaviour and ATCO intentions.
  arXiv  Detail & Related papers  (2023-09-26T14:20:09Z)
• Learning Generative Models for Climbing Aircraft from Radar Data [0.0]
  This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 66.3% less error when compared to BADA; generated trajectories that are realistic when compared to test data.
  arXiv  Detail & Related papers  (2023-09-26T13:53:53Z)
• Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures [52.25258289718559]
  We propose a probabilistic model that can learn the variability from procedural data and flight tracks collected from radar surveillance data. We show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft.
  arXiv  Detail & Related papers  (2023-03-17T13:58:06Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes [0.0]
  The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. New airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems.
  arXiv  Detail & Related papers  (2021-03-04T23:16:56Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.