Next Article in Journal
A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
Previous Article in Journal
An Upper Bound of the Third Hankel Determinant for a Subclass of q-Starlike Functions Associated with k-Fibonacci Numbers
Open AccessArticle

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

1
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
2
Department of Signal Processing and Communications, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain
3
LATUV, Remote Sensing Laboratory, Universidad de Valladolid, 47011 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(6), 1045; https://doi.org/10.3390/sym12061045
Received: 19 May 2020 / Revised: 19 June 2020 / Accepted: 21 June 2020 / Published: 23 June 2020
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. View Full-Text
Keywords: low-visibility events; radiation fog; persistence analysis; detrended fluctuation analysis; markov chains; machine learning algorithms low-visibility events; radiation fog; persistence analysis; detrended fluctuation analysis; markov chains; machine learning algorithms
Show Figures

Figure 1

MDPI and ACS Style

Cornejo-Bueno, S.; Casillas-Pérez, D.; Cornejo-Bueno, L.; Chidean, M.I.; Caamaño, A.J.; Sanz-Justo, J.; Casanova-Mateo, C.; Salcedo-Sanz, S. Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain. Symmetry 2020, 12, 1045.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop