State estimation in large-scale open channel networks using particle filters

We consider the problem of estimating flow state in real time 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 using some additional flow measurements. The estimation method is implemented using a model of a network of 19 subchannels and one reservoir, Clifton Court Forebay, in Sacramento-San Joaquin Delta in California and the numerical results are presented.