Data Assimilation of Mobile Sensors in Hydrological Models of Unsteady Flow

摘要

In this paper, the estimation of the spatio-temporal variation of water bodies for statevariables,velocity (m/s) and water surface elevation(m) for unsteady flows in open channelshas been investigated. For data assimilation, average velocity measurements are obtained frommobile sensors such as Lagrangian sensors which have the ability to float passively in waterbodics and provide their GPs location.One dimensional Saint-Venant equations are used fora system model linearized by a 'Taylor series expansion. To obtain a discrete-time state-spacemodel, the coupled PDEs are discretized by Lax difusive method in time and space. For stateestimation of the open channel, a Kalman filter is set up with suitable filtering parameters forthe channel's model.Eulerian (fixed) sensors present at the head and tail of the canal providethe minimally required boundary conditions to run the model. The system is simulated usingHEC-RAS simulation software. Water velocity profiles are used to predict the movement of thefloat, providing measurements for the Kalman Filter which is run in MATLAB. The estimatedstates are compared with actual values.