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Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models |
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| Authors: | Balew A. Mekonnen Alireza Nazemi Kerry A. Mazurek Amin Elshorbagy Gordon Putz |
| Affiliation: | 1. Department of Civil and Geological Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N 5A9, Canada;2. Global Institute for Water Security, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK, S7N 3H5, Canada;3. Global Institute for Water Security, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK, S7N 3H5, Canada |
| Abstract: | AbstractMuch of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes. Editor D. Koutsoyiannis; Associate editor L. See |
| Keywords: | prairie hydrology unconventional runoff generation hybrid modelling Soil and Water Assessment Tool (SWAT) artificial neural network (ANN) |