期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 60, 期 9, 页码 1473-1489出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2014.935778
关键词
prairie hydrology; unconventional runoff generation; hybrid modelling; Soil and Water Assessment Tool (SWAT); artificial neural network (ANN)
资金
- Province of Saskatchewan Go Green Fund
- Canada Excellence Research Chair in Water Security at the University of Saskatchewan
Much 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.
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