State Estimation in Large-Scale Open Channel Networks Using Sequential Monte Carlo

Mohammad Rafiee, UC Berkeley Mechanical Engineering
October 26th, 2011 at 4PM–5PM in 939 Evans Hall [Map]

We investigate the performance of sequential Monte Carlo estimation methods for estimation of flow state in large-scale open channel networks. After constructing a state space model of the flow based on the Saint-Venant equations, we implement the optimal sequential importance resampling (SIR) filter to perform state estimation in a case in which measurements are available every time step. Considering a case in which measurements become available intermittently, a random map implementation of the implicit particle filter is applied to estimate the state trajectory in the interval between the measurements. Finally, some heuristics are proposed which are shown to improve the estimation results and lower the computational cost. In the first heuristics, considering the case in which measurements are available every time step, we apply the implicit particle filter over time intervals of a desired size while incorporating all the available measurements over the corresponding time interval. As a second heuristic, we apply an approximate maximum a posteriori (MAP) method which does not require sampling. It is seen, through implementation, that the MAP method provides more accurate results in the specific case of our application while having a smaller computational cost. All estimation methods are tested on a network of 19 subchannels and one reservoir, Clifton Court Forebay, in Sacramento-San Joaquin Delta in California and numerical results are presented.