Predicting dust storms using hybrid intelligence system

Abstract

Global dust storm events seem to increase and become more severe year over year. Thus, dust storm event understanding in terms of causes, pre-ignition signals, generation processes, and procedures can be of great significance due to the impact they can have to the society. Dust storm behaviours is usually based on five attributes mainly. These are wind speed, pressure, temperature, humidity and surface condition. Dust storm may affect both rural and urban life conditions since they can cause significant difficulties to outdoor activities in low visibility – high degree of danger weather. However, dust storm predictions using historical storm data has not been used yet effectively. This study examines the process of predicting and identifying dust storms using past storm events through a novel combination of Bayesian networks (BNs), case-based reasoning (CBR) approach and rule based system (RBS) techniques.