Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain

• Main Authors: Sara Cornejo-Bueno, David Casillas-Pérez, Laura Cornejo-Bueno, Mihaela I. Chidean, Antonio J. Caamaño, Julia Sanz-Justo, Carlos Casanova-Mateo, Sancho Salcedo-Sanz
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Symmetry
• Subjects: low-visibility events; radiation fog; persistence analysis; detrended fluctuation analysis; markov chains; machine learning algorithms
• Online Access: https://www.mdpi.com/2073-8994/12/6/1045

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.