Application of Artificial Neural Networks for Fog Forecast


  • Rosangela de Oliveira Colabone Academia da Força Aérea
  • Antonio Luiz Ferrari Academia da Força Aérea
  • Adriano Rogério Bruno Tech Universidade de São Paulo
  • Francisco Arthur da Sila Vecchia Universidade de São Paulo


Strategic planning, Operational management, Intelligent systems, Decision support systems


This study examines the development of a system that assists in planning flight activities of the Academia da Força Aérea (AFA) so that meteorological data can be used to predict the occurrence of fog. This system was developed in MATLAB 8.0 by applying multilayer perceptron-type artificial neural networks and using an error correction algorithm called backpropagation. The methodology used to implement the network comprises eight input variables, five neurons in the intermediary layer, and one neuron in the output layer, which corresponds to the presence or absence of fog. The fog phenomenon is very important for the study and definition of flight strategic planning. Data taken from 1989 to 2008 and related to the input variables were used for the training and validation of the proposed network. Consequently, the multilayer perceptron network has a 95% reliability compared with the data collected. This high level of reliability is an exceptional result for the management, planning, and decision making team of the AFA strategic group. Thus, it can be concluded that the proposed system is efficient and will subsidize, with good safety margin, AFA’s flight activity planning and could also be applied to other air activities in Brazil.

Author Biographies

Rosangela de Oliveira Colabone, Academia da Força Aérea

Área de Ciências Exatas

Antonio Luiz Ferrari, Academia da Força Aérea

Área de Ciências Exatas

Adriano Rogério Bruno Tech, Universidade de São Paulo

Faculdade de Zootecnia e Engenharia de Alimentos

Francisco Arthur da Sila Vecchia, Universidade de São Paulo

Escola de Engenharia de São Carlos/Departamento de Hidráulica e Saneamento






Original Papers