Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This study presents a prediction system based on evolving fuzzy models and a bottom-up approach for daily streamflow forecasting. Prediction models are based on adaptive Takagi-Sugeno fuzzy inference systems. These models make use of a sequential learning approach for updating their own structure and parameters over time according to changes or variations identified in the series, whereas rainfall and runoff information is processed at each time instant. Models are adjusted following a bottom-up approach, which consists of dividing the global problem into sub-problems, and each sub-problem is resolved separately. Final estimate is given by the aggregation of the parts. The proposed approach is compared to the Soil Moisture Accounting Procedure (SMAP), a hydrological model used by various hydroelectric companies of the Brazilian electrical sector. Simulation studies indicate that the evolving fuzzy system presents an adequate performance, leading to a promising alternative for daily streamflow forecasting. Indeed, results are improved when predictors are combined, primarily for a multistep ahead prediction task. © 2009 IEEE. CNPq,CAPES,Araucaria FAPEMIG - Brazilian res. funding agencies Found.,COPEL,Itaipu Power PlantConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Hong, Y.-S.T., White, P.A., Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm (2009) Advances in Water Resources, 32, pp. 110-119Solomatine, D.P., Siek, M.B., Modular learning in forecasting natural phenomena (2006) Neural Networks, 19 (2), pp. 215-224Zambelli, M., Luna I, Soares, S., Long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models (2009) Procs. of the PowerTech Conference, pp. 1-8Price, R., (2008) Lecture on knowing the context between hydroinformatics and flood modelling, , UNESCO-IHE Institute for Water Edutation, Tech. Rep., NovemberZealand, C.M., Burn, D.H., Simonovic, S.P., Short term streamflow forecasting using artificial neural networks (1999) Journal of Hydrology, 214, pp. 32-48Nayak, P., Sudheer, K., Rangan, D., Ramasastri, K., A neuro-fuzzy computing technique for modeling hydrological time series (2004) Journal of Hydrology, 291, pp. 52-66Bowden, G.J., Maier, H.R., Dandy, G.C., Input determination for neural network models in water resources applications. Part 2. Case Study: Forecasting salinity in a river (2005) Journal of Hydrology, (301), pp. 93-107Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Transactions on Systems, Man and Cybernetics, 1, pp. 116-132Luna, I., Soares, S., Ballini, R., An adaptive hybrid model for monthly streamflow forecasting (2007) Proceedings of The IEEE International Conference on Fuzzy Systems, pp. 1-6Lopes, J., Braga, B., Conejo, J., (1982) SMAP - A Simplified Hydrological Model, Applied Modelling in Catchment Hydrology, , Ed. V.P.Singh, Water Resourses PublicationsDawson, C., Wilby, R., Hydrological modelling using artificial neural networks (2001) Progress in Physical Geography, 25 (1), pp. 80-108Chiu, S., A cluster estimation method with extension to fuzzy model identification (1994) Proceedings of The IEEE International Conference on Fuzzy Systems, 2, pp. 1240-1245. , JuneAngelov, P.P., Filev, D.P., An approach to online identification of Takagi-Sugeno fuzzy models (2004) IEEE Transactions on Systems, Man and Cybernetics- Part B, 34 (1), pp. 484-498Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixture of local experts (1991) Neural Computation, 3 (1), pp. 79-87Er, M.J., Wu, S., A fast learning algorithm for parsimonious fuzzy neural systems (2002) Fuzzy Sets and Systems, 126, pp. 337-351Wang, L., (1994) Adaptive Fuzzy Systems and Control, , Prentice HallHaykin, S., (2001) Kalman Filtering and Neural Networks, , John Wiley & Sons, IncAitken, A., Assessing systematic errors in Rainfall-Runoff models (1973) Journal of Hydrology, 20, pp. 121-136