EFFECTIVE APPLICATION OF A REAL-TIME RUNOFF PREDICTION SYSTEM USING A PARTICLE FILTER

Abstract

 A real-time rainfall-runoff prediction system was developed using the storage function method incorporating with a particle filter. The performance of the prediction system was examined at the upper part of the Yattajima River basin (5150 km²) in terms of several ways to incorporate observation data, state variables to give system noise (storage variables of basin, storage variables of river, and both of them), and time intervals for data assimilation. Our findings show that to assimilate state variables for each sub-basin provides better performance than to assimilate all state variables at the same time; to give system noise to catchment and river storage variables shows better performance for longer prediction time; and setting assimilation time intervals 10 minutes is better than 1 hour.