Ordinal regression algorithms for the analysis of convective situations over Madrid-Barajas airport

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Áreas de investigación:
Año:
2020
Tipo de publicación:
Artículo
Palabras clave:
Convective clouds, Convective analysis, Airports, Machine learning techniques, Ordinal regression
Autores:
Journal:
Atmospheric Research
Volumen:
236
Páginas:
104798
Mes:
Mayo
ISSN:
0169-8095
Nota:
JCR(2020): 5.369 Position: 16/94 (Q1) Category: METEOROLOGY & ATMOSPHERIC SCIENCES
Abstract:
In this paper we tackle a problem of convective situations analysis at Adolfo-Suarez Madrid-Barajas International Airport (Spain), based on Ordinal Regression algorithms. The diagnosis of convective clouds is key in a large airport like Barajas, since these meteorological events are associated with strong winds and local precipitation, which may affect air and land operations at the airport. In this work, we deal with a 12-h time horizon in the analysis of convective clouds, using as input variables data from a radiosonde station and also from numerical weather models. The information about the objective variable (convective clouds presence at the airport) has been obtained from the Madrid-Barajas METAR and SPECI aeronautical reports. We treat the problem as an ordinal regression task, where there exist a natural order among the classes. Moreover, the classification problem is highly imbalanced, since there are very few convective clouds events compared to clear days. Thus, a process of oversampling is applied to the database in order to obtain a better balance of the samples for this specific problem. An important number of ordinal regression methods are then tested in the experimental part of the work, showing that the best approach for this problem is the SVORIM algorithm, based on the Support Vector Machine strategy, but adapted for ordinal regression problems. The SVORIM algorithm shows a good accuracy in the case of thunderstorms and Cumulonimbus clouds, which represent a real hazard for the airport operations.
Comentarios:
JCR(2020): 5.369 Position: 16/94 (Q1) Category: METEOROLOGY & ATMOSPHERIC SCIENCES
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