SEQUENTIAL ESTIMATION OF RIVER WATER LEVEL AND RIVERBED FLUCTATION USING A PARTICLE FILTER

Abstract

 Real-time flood forecasting requires a hydraulic model that understands river channel characteristics and a hydrological model that ensures a high level of accuracy. In this study, we proposed a particle filter method capable of simultaneously estimating river-bed evolution at the water level gauging station and slope water depth, which is the initial conditions for the RRI model. The target river is the Seri River in Shiga Prefecture, Japan, in which sediment deposition is a concern. We conducted experiments using meteorological and hydrological data collected when Typhoon No. 18 hit the country in September 2013. As a result, we confirmed that the calculated water level at the present time can be assimilated to the observed water level, and that the estimated changes in river-bed evolution can be explained from a sediment-transport theory and river characteristics. In conclusion, the practicality and validity of the method we proposed in this study were verified.