The issue of demand calibration and estimation under uncertainty is known to be an exceptionally difficult problem in water distribution system modelling. In the context of real-time event modelling, the stochastic behaviour of the water demands and non-geographical distribution of the demand patterns makes it even more complicated. This paper considers a predictor - corrector approach, implemented by a particle filter model, for solving the problem of demand multiplier factor estimation. A demand forecasting model is used to predict the water demand multiplier factors. The EPANET hydraulic solver is applied to simulate the hydraulic behaviour of a water network. Real time observations are integrated via a formulation of the particle filter model to correct the demand predictions. A water distribution network of realistic size with two configurations of demand patterns (geographically distributed demand patterns and non-geographically distributed demand patterns) are used to evaluate the particle filter model. Results show that the model is able to provide good estimation of the demand multiplier factors in a near real-time context if the measurement errors are small. Large measurement errors may result in inaccurate estimates of the demand values.