Application of Artificial Neural Networks for Fog Forecast

Abstract

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.

A review on factors influencing fog formation, classification, forecasting, detection and impacts

“…Applicability of ANN was tested for predicting fog in the Canberra International Airport where the designing, training, testing, and validation using the historical observational data from the airport and recommended being used in other airports (Fabbian et al 2007 ). A similar kind of practice was adopted for predicting visibility for nowcasting fog visibility at Kolkata airport; the study considered pollutants and meteorological data spanning eleven years where the predictors were decided with the help of decision tree algorithm and then used as input in ANN model using the MLP (Multilayer Perceptron) backpropagation learning method for visibility prediction (Dutta and Chaudhuri 2015 ), in Academia da Força Aérea (Colabone et al 2015 ) in International Airport of MACEIÓ and Brazil (Costa et al 2006 ). Another case study of Wamena Airport utilised a bunch of algorithms including Xtreme Randomized Tree (XRT), Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), Deep Learning (DP, Distributed Random Forest (DRF), and stacked Ensemble comprising of all the model’s aforementioned which gave the best result (Dewi and Harsa 2020 ).…”

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

“…Then, a pioneering method for very short-term forecasting [13] was developed based on neural network algorithms to predict the Vis and H c at Guarulhos Airport, São Paulo. Similarly, a fog prediction method based on a ML algorithm was developed for the Brazilian Air Force aerodrome at Pirassununga using meteorological observation data collected from 1989 to 2008, and it was concluded that the suggested neural network algorithm predictions are 95 percent equivalent to observations [14]. A series of works [15][16][17], explored the use of ML algorithms for short-term forecasting of convective events for the Rio de Janeiro metropolitan region.…”

Sea Fog Dissipation Prediction in Incheon Port and Haeundae Beach Using Machine Learning and Deep Learning

“…Neural networks have been used for fog prediction for a long time with varied success. Unlike other domains, fog data are relatively scarce, while neural networks work best in a data abundance regime [ 8 , 9 , 10 ]. Furthermore, fog occurs seldom and leads to a data imbalance, with most data being in the non-fog cluster.…”