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Data-driven models are relatively simple but powerful methods for river flow prediction; to use the strengths of each, hybrid model-free methods are proposed. In this study, a feature-based adaptive combiner (FBAC) is introduced that uses features extracted from the data to provide a robust meta-model for river flow prediction. Artificial neural network (ANN), random forest regression (RFR), support vector regression (SVR), and a modified dynamic K-nearest neighbours (DKNN) are used to construct the FBAC. DKNN is incorporated to assess the flexibility of the FBAC in using novel data-driven approaches. The FBAC’s performance is evaluated using two years of the daily inflow to the Azad Reservoir in western Iran. Results indicate that the FBAC shows 31.8% and 29.5% improvement in accuracy, in terms of a reduction in the Root Mean Square Error (RMSE) value, compared to the best individual and combined model, respectively. For extreme value prediction, improvements are increased to 35.1% and 32.6%, respectively.

A feature-based adaptive combiner for coupling meta-modelling techniques to increase accuracy of river flow prediction