Applications of Kalman Filtering and Maximum Likelihood Parameter Identification to Hydrologic Forecasting,
1980
Goldstein, Jacob D | Larimore, Wallace E
The applications of the canonical variate, Kalman filtering and maximum likelihood parameter identification techniques to the requirements of the National Weather Service in river flow forecasting are investigated. State space reduced-order models for unit hydrographs are obtained with the use of canonical variate methods. A complete state-space model for a catchment consisting of the Sacramento model as the soil moisture system and the basin's unit hydrograph as the channel routing system is constructed. This model is used in the design of extended Kalman filters for the prediction of the channel discharge and the state of the system, and also in the design of an algorithm for the identification of catchment model parameters through the use of maximum likelihood techniques. The performance of the algorithms is demonstrated with synthetic data generated with the models for the Bird Creek and White River basins.
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