Print Email Facebook Twitter The ensemble particle filter (EnPF) in rainfall-runoff models Title The ensemble particle filter (EnPF) in rainfall-runoff models Author Van Delft, G. El Serafy, G.Y. Heemink, A.W. Faculty Electrical Engineering, Mathematics and Computer Science Department Delft Institute of Applied Mathematics Date 2009-01-13 Abstract Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own strengths. Research was carried out to a possible combination between both types of filters that will lead to a new type of filters that joins the strengths of both. The so called ensemble particle filter (EnPF) new combination is tested on flood forecasting problems in both the hindcast mode as well as the forecast mode. Several proposed combinations showed considerable improvement when a hindcast comparison on synthetic data was considered. Within the forecast comparison with field data, the suggested EnPF showed remarkable improvements compared to the PF and slight improvements compared to the EnKF. Subject Rainfall-runoff modelsEnsemble Kalman filterParticle filterEnsemble particle filter To reference this document use: http://resolver.tudelft.nl/uuid:c312e101-4e23-49d2-988d-00a9cd621031 Publisher Springer ISSN 1436-3259 Source Stochastic Environmental Research and Risk Assessment, 23 (8) Part of collection Institutional Repository Document type journal article Rights (c) 2009 Springer-Verlag Files PDF van_delft_2009.pdf 441.1 KB Close viewer /islandora/object/uuid:c312e101-4e23-49d2-988d-00a9cd621031/datastream/OBJ/view