## UC Berkeley / Lawrence Berkeley Laboratory

#### 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.