Abstract: Mass gatherings emerging both for specific occasions and spontaneously, are naturally associated with the risk of stampedes and crowd clashes that may trigger dramatic consequences in shorter or longer term perspectives. In order to address such issues the present paper suggests the application of the agent-based modeling approach to short-term predictions of future states of large congregates of people. The latter is of prime value for practitioners who seek to identify the potentially dangerous areas where the risk of stampede-induced injuries is assumed the highest based on the estimations of the crowd pressure at a given spot. In this paper, we outline the algorithm for generating forecasts and on its basis, propose a system of decision support. The test of system applicability has been performed based on the 2018 World Football Championship stadium use case. The object under investigation is expected to be put into operation in 2018.

Abstract: This paper focuses on techniques for dealing with imperfect data in a frame of early warning system (EWS). Despite the fact that data may be technically damaged by presenting noise, outliers or missing values, met-ocean simulation systems have to deal with them to provide data transaction between models, real time data assimilation, calibration, etc. In this context data quality-control becomes one of the most important parts of EWS. St. Petersburg FWS was considered as an example of EWS. Quality control in St. Petersburg FWS contains blocks of technical control, human mistakes control, statistical control of simulated fields, statistical control and restoration of measurements and control using alternative models. Domain specific quality control was presented as two types of procedures based on theoretically proved methods were applied. The first procedure is based on probabilistic model of dynamical system, where processes are spatially interrelated and could be implemented in a form of multivariate regression (MRM). The second procedure is based on principal component analysis extended for taking into account temporal relations in data set (ePCA).