Bayesian System Identification of a River

Abstract

Hydrological systems like rivers and lakes are a vital part of any community. Mathematical models of such systems underpin management, decision making and control of rivers. There is always uncertainty associated with the models, and in this paper we consider Bayesian system identification of a river. Bayesian system identification delivers an a-posteriori probability distribution of the unknown parameters. This uncertainty description is useful for solving chance-constrained problems encountered in the control and management of rivers. The Bayesian system identification approach is demonstrated by applying it to the upper part of the Murray River in Australia using real water level and flow measurements. The obtained models show good simulation performance capturing the observed water levels well. Moreover, posterior distributions of the parameters are delivered which are useful for control of rivers.