文章基本信息

标题：State Estimation in 2D Hydrological Models using Lagrangian Sensors and Low Resolution Elevation Maps
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作者：Affan Affan ; Hasan Arshad Nasir ; Abubakr Muhammad 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：16549-16554
DOI：10.1016/j.ifacol.2020.12.777
语种：English
出版社：Elsevier
摘要：AbstractIn this research work, the framework to estimate 2D spatio-temporal variation in hydrodynamic variables such as water velocity (m/s) and water level (m) in complex, large scale open channels has been investigated using Lagrangian sensors. The Lagrangian sensors are passive floating platforms, which report its GPS position along with the flow of water. The 2D Saint-Venant model is simulated using HEC-RAS simulation software, the geometrical details for HEC-RAS simulations are obtained using Digital Elevation Map (DEM) of Ravi river, Pakistan. For the system model, the non-linear 2D Saint-Venant model is augmented with a Lagrangian sensor motion model. For state estimation, the GPS position of the Lagrangian sensor along with the upstream water level is assimilated in the augmented model using an Ensemble Kalman Filter (EnKF) with suitable filtering parameters in MATLAB. The hydrodynamic variables and trajectory of the Lagrangian sensor are estimated with low error.
关键词：KeywordsData AssimilationEnsemble Kalman Filter2D Saint-Venant ModelLagrangian SensorsDigital Elevation Maps (DEMs)