The optimum "ltering results of Kalman "ltering for linear dynamic systems require an exact knowledge of the process noise covariance matrix Q I , the measurement noise covariance matrix R I and the initial error covariance matrix P . In a number of practical solutions, Q I , R I and P , are either
Application of the Kalman filter to the Nash model
โ Scribed by Y. H. Lee; V. P. Singh
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 202 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0885-6087
No coin nor oath required. For personal study only.
โฆ Synopsis
The Nash model was used for application of the Kalman ยฎlter. The state vector of the rainfallยฑruno system was constituted by the IUH (instantaneous unit hydrograph) estimated by the Nash model and the runo estimated by the Nash model using the Kalman ยฎlter. The initial values of the state vector were assumed as the average of 10% of the IUH peak values and the initial runo estimated from the average IUH. The Nash model using the Kalman ยฎlter with a recursive algorithm accurately predicted runo from a basin in Korea. The ยฎlter allowed the IUH to vary in time, increased the accuracy of the Nash model and reduced physical uncertainty of the rainfallยฑruno process in the river basin.
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