Author(s)

K. Ponnambalam , A.W. Heemink, and J.G.M. Schoenmakers

Abstract

Decision makers often want engineers to estimate risk in environmental systems; risk here is defined as, for example, the probability of exceeding a certain concentration of a pollutant at a certain place and time. Unfortunately, this is a very hard problem when modeling real- world systems because of the use of nonlinear models, uncertainty in system parameters, stochastic inputs, noisy measurements, and the large scale nature of the problems. In this paper, after reviewing some of the state-of-the-art techniques in stochastic modelling we present two new techniques to estimate risk in some environmental systems. We present the application of these techniques to an example in water pollution modelling. We als

Keywords