Prediction of convective clouds formation using evolutionary neural computation techniques

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Research areas:
Year:
2020
Type of Publication:
Article
Keywords:
Convection initialization prediction, machine learning algorithms, neural networks, unbalanced databases
Authors:
Journal:
Neural Computing and Applications
Volume:
32
Pages:
13917-13929
Month:
February
ISSN:
0941-0643
Note:
JCR(2020): 5.606 Position: 31/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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, among others. 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 hours. 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.
Comments:
JCR(2020): 5.606 Position: 31/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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