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Severe haze or low visibility event caused by particulate pollution hasbecome a serious environmental issue in Southeast Asia. A forecasting frameworkof such events based on deep convolutional neural networks has been developed.The framework has been trained using time sequential maps of up to 18meteorological and hydrological variables alongside surface visibility dataover past 35 years. In forecasting haze versus no-haze situations in Singapore,the trained machine has achieved a good overall accuracy that easily exceedsthat of the no-skill blinded forecast based on haze occurrence frequency.However, the machine still produces a relatively high number of missingforecasts (false negative for haze events), likely owing to its lack ofexperience in identifying atypical patterns. Nevertheless, this effort hasdemonstrated a promising prospect of using deep learning algorithms to predictthe occurrence of extreme environmental and weather events, and to advanceknowledge about these still poorly known phenomena.

Exploiting deep learning in forecasting the occurrence of severe haze in Southeast Asia