1. Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain
- Author
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C. Casanova-Mateo, Antonio J. Caamaño, David Casillas-Perez, Sancho Salcedo-Sanz, Mihaela I. Chidean, Laura Cornejo-Bueno, Julia Sanz-Justo, and Sara Cornejo-Bueno
- Subjects
Persistence (psychology) ,010504 meteorology & atmospheric sciences ,Physics and Astronomy (miscellaneous) ,Computer science ,General Mathematics ,010502 geochemistry & geophysics ,01 natural sciences ,Runway visual range ,machine learning algorithms ,Statistics ,Computer Science (miscellaneous) ,Visibility ,0105 earth and related environmental sciences ,Event (probability theory) ,detrended fluctuation analysis ,markov chains ,Series (mathematics) ,Markov chain ,lcsh:Mathematics ,lcsh:QA1-939 ,Mixture of experts ,Chemistry (miscellaneous) ,Detrended fluctuation analysis ,persistence analysis ,low-visibility events ,radiation fog - Abstract
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.
- Published
- 2020