Paper Info
Title
Prediction of convective clouds formation using evolutionary neural computation techniques
Abstract
The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities. In this paper, we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time horizon of 12 h. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (sigmoidal unit, product unit and radial basis function) and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic.
Year
DOI
Venue
2020
10.1007/s00521-020-04795-w
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Convection initialisation prediction,Machine learning algorithms,Neural networks,Unbalanced databases
Journal
32.0
Issue
ISSN
Citations 
17
0941-0643
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
David Guijo-Rubio112.06
Pedro A. Gutiérrez2413.16
Carlos Casanova-Mateo3253.23
Juan Carlos Fernández4729.77
Antonio Manuel Gómez-Orellana500.34
Pablo Salvador-González600.34
Sancho Salcedo-Sanz758071.21
César Hervás-Martínez879678.92