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Understanding the evolving discipline of urban fire situations is a basic but challenging task for urban security and fire-fighting decisions. Traditional methods forecast the urban fire situation through mathematical modeling and statistical learning, which could be interpretable but generally lack of efficiency and practicality. Recently, some deep neural network methodologies, especially convolutional neural network (CNN) and recurrent neural network (RNN), are presented as paradigms to capture dynamics in spatial–temporal complex phenomenon, which tally with the characteristics of fire situation forecasting. In this paper, we propose a novel deep sequence learning model as the fire situation forecasting network (FSFN) to better process the information and spatio-temporal correlations in regional urban fire alarm dataset. FSFN model integrates structures of Variational auto-encoders and context-based sequence generative model Seq2seq to obtain the latent representation of the fire situation and learn the spatio-temporal dynamics. Furthermore, we augment the network structure of FSFN from a simple deep sequence generative model to adversarial fire situation forecasting network with auxiliary information(Adversarial FSFN-A). The experimental studies demonstrate the effectiveness of Adversarial FSFN-A has superior spatio-temporal distribution prediction of multi-type urban fire situation. • It is the first exploration to process large-scale urban fired dataset with deep sequence learning models. • We first proposed FSFN to capture the spatio-temporal latent dynamics of fires. • We proposed Adversarial FSFN-A based on FSFN, which incorporates the offline geographical, social attributes and spatio-temporal dependencies. [ABSTRACT FROM AUTHOR]

The notions of multi-granulation and multi-scale are two important issues for the granular computing, both of which can describe the granular structure in certain ways. In this paper, we investigate the belief structure and the plausibility structure by defining belief and plausibility functions from the multi-granulation viewpoint, and discuss how to construct multi-granulation rough set (MGRS) models in multi-scale information systems (MSISs). Based on the MGRS in MSISs, the optimal scale selection methods with various requirements are studied in two aspects of optimistic and pessimistic multi-granulation for a multi-scale decision information system (MSDIS). To interpret and understand the proposed theories, some important properties of optimistic and pessimistic multi-granulation optimal scale selection are analyzed for the MSDIS, which could indicate the inner relationships among the different selection methods of the optimal scales. Furthermore, an example is provided to illustrate and verify these investigated properties. • Multigranulation-based belief and plausibility functions and structures are proposed. • Multigranulation rough sets in multi-scale information systems are presented. • Multigranulation-based optimal scale selection for multi-scale information systems are discussed. • Important properties of multigranulation-based optimal scale selection are analyzed. [ABSTRACT FROM AUTHOR]