A nonlinear data-driven model for synthetic generation of annual streamflows

A hybrid model that blends two non-linear data-driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non-parametric resampling technique MBB to obtain innovations, which are then added back to the ANN-modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN-based non-linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN-based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric-based hybrid model (LPHM), owing to the effective capturing of the non-linearities. Moreover, the ANNHM, being a completely data-driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non-linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright (c) 2007 John Wiley & Sons, Ltd.