Application of nonlinear filtering in the real time forecasting of river flows

Abstract

The practical use of nonlinear filters on conceptual stochastic watershed models is investigated. Results obtained on a case study emphasize the importance of the assumed noise components of the watershed model. Depending on such noises, runoff predictions could range from excellent to unacceptable. The simpler of the nonlinear filters considered, the extended Kaiman filter, was found to be, when effective, as good as other, more complicated filters. Although smoothing algorithms lead to improvements, their computational burden may be unacceptable.