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BayesNet and Artificial Neural Network for Nowcasting Rare Fog Events

Authors:
Gaetano Zazzaro
Paola Mercogliano
Gianpaolo Romano

Keywords: Data Mining; Forecast Fog; Bayesian Networks, Artificial Neural Networks; Knowledge Discovery in Database Process; Weka; CRISP-DM

Abstract:
Fog represent high impact atmospherical phenomena especially for aviation. In particular, in 2001 the Linate Airport in Milan was interested by a disaster, the deadliest air disaster to ever occur in Italian aviation history, due to un-forecasted thick fog. For this reason, improvement of fog monitoring and forecast tool is a challenge topic for the aviation community. Moreover, forecasting fog is an important issue for air traffic safety because adverse visibility conditions represent one of the major causes of traffic delay and of the economic loss associated with such phenomena. In such context, the present work illustrates a Data Mining application for the fog forecast on a short time range (1 hour) on Linate airport. Indeed two predictive models have been trained using an historical dataset of 18 years of fog observations and other relevant meteorological parameters collected in the Synop message by applying BayesNet and Neural Network algorithms. The performances evaluation shows the complete model for fog events forecasting presents 90% of instances correctly predicted. The work has been carried on according to the standard process (CRISP-DM) for Knowledge Discovery in Database Process.

Pages: 84 to 90

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-558-6

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017