A pilot study for an enhanced algal spatial pattern prediction using RS images

Conference paper (2009)

Authors

Hong Li WL Delft Hydraulics, IHE Delft Institute for Water Education
Mijail Arias IHE Delft Institute for Water Education
Steef Peters Vrije Universiteit Amsterdam
A.E. Mynett IHE Delft Institute for Water Education, WL Delft Hydraulics, Environmental Fluid Mechanics - CEG
Research Group
Environmental Fluid Mechanics (CEG) (TU Delft)
Kalman filter Expected value of system noise Gain matrix Real-time correction
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Published Date

01-01-2009

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Hydraulic Engineering

Research Group

Environmental Fluid Mechanics

Abstract

Accurate and reliable flow forecasting form an important basis for efficient real-time river management, including flood control, flood warning and so on. In order to improve the accuracy of flow forecasting, gain matrix of Kalman filter was applied to real-time correction of hydraulic model for spatial distributing the system deviation (called expected value of system noise in Kalman filter). That means Kalman gain matrix is used to distribute model system deviation from measurement cross sections to the entire state of the river system. State functions of Kalman filter were set up based on discretization and linearization Saint-Venant equations by adopting four-point linear implicit form, and the spatial distribution system deviation method (SDM) was used for real-time correction. The calculation of flood forecasting for river section from Cuntan to Fengjie of Yangtze River verifies that SDM is useful in promoting the accuracy of real-time flood forecasting.