HYDROLOGICAL PROCESSES Hydrol. Process. 19, 3819–3835 (2005) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.5983 Rainfall-runoff models using artiﬁcial neural networks for ensemble streamﬂow prediction Dae-Il Jeong and Young-Oh Kim* School of Civil, Urban & Geosystems Engineering, Seoul National University, San 56-1, Shillim-Dong, Gwanak-gu, Seoul 151-742, South Korea Abstract: Previous ensemble streamﬂow prediction (ESP) studies in Korea reported that modelling error signiﬁcantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall-runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artiﬁcial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall-runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inﬂows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root-mean-square error. The overall results showed that the ENN performed the best among the three rainfall-runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half-Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall-runoff modelling than TANK or an SNN. Copyright  2005 John Wiley & Sons, Ltd. KEY WORDS artiﬁcial neural networks; ensemble neural network; ensemble streamﬂow prediction; probabilistic forecasting; rainfall-runoff model INTRODUCTION Introduced in the 1970s, ensemble streamﬂow prediction (ESP) became a key part of the advanced hydrologic prediction system for the National Weather Service in the USA. ESP inputs historical meteorological scenarios to a rainfall-runoff model to forecast future streamﬂows using the current soil moisture, river, and reservoir conditions. Therefore, it is generally true that the accuracy of ESP forecasts relies primarily on the rainfall- runoff model being used. Jeong and Kim (2002) conﬁrmed that the rainfall-runoff model used for their ESP study did not perform well for winter and spring, i.e. the dry season, and thus suggested that the model should be improved to obtain more accurate probabilistic ESP forecasts. The present study proposed another rainfall-runoff model using artiﬁcial neural networks (ANNs), which can be used for ESP. Once this ANN rainfall-runoff model is proven to perform reasonably well, it can be substituted for or combined with (Kim et al., 2003) the existing model to improve the performance of the existing ESP. ANNs have been widely used for various aspects of hydrology: the ASCE Task Committee on Artiﬁcial Neural Networks in Hydrology (2000) and Govindaraju and Rao (2000) reviewed ANN theories and applications in hydrology. Previous studies have demonstrated that ANNs are appropriate for complex nonlinear rainfall-runoff modelling (Hsu et al., 1995; Minns and Hall, 1996; Shamseldin, 1997; Sajikumar and Thandaveswara, 1999; Tokar and Johnson, 1999), streamﬂow forecasting (Karunanithi et al., 1994; Campolo et al., 1999a,b; Zealand et al., 1999; Zhang and Govindaraju, 2000; Kim and Barros, 2001; Birikundavyi et al., * Correspondence to: Young-Oh Kim, School of Civil, Urban and Geosystems Engineering, Seoul National University, San 56-1, Shillim- Dong, Gwanak-gu, 151-742 Seoul, Korea. E-mail: yokim05@snu.ac.kr Received 3 February 2004 Copyright  2005 John Wiley & Sons, Ltd. Accepted 22 June 2005