Abstract
Discharge time series were investigated using predictive models of support vector machine (SVM) and artificial neural network (ANN) and their performances were compared with two conventional models: rating curve (RC) and multiple linear regression (MLR) techniques. These models are evaluated using stage and discharge data from Big Cypress River, Texas, USA. Daily river stage–discharge data for the period of April 2010 to August 2013 were used for training and testing the above models and their results were compared using appropriate performance criteria. The evaluation of the results includes different performance measures, which indicate that SVM and ANN have an edge over the results by the conventional RC and MLR models. Notably, peak values predicted by SVM and ANN are more reliable than those by RC and MLR, although the performances of these conventional models are acceptable for a range of practical problems. The paper projects a critical view on inter-comparison studies by seeing through model selection approaches based on the common practice of the absolute best or even the best for the stated purpose towards uncertainty analysis.
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Acknowledgments
The data used in this study were downloaded from the web server of the USGS. The author wishes to thank the staff of the USGS who are associated with data observation, processing, and management of USGS Web sites. Thanks are also due to the anonymous reviewers for many useful suggestions.
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Ghorbani, M.A., Khatibi, R., Goel, A. et al. Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci 75, 685 (2016). https://doi.org/10.1007/s12665-016-5435-6
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DOI: https://doi.org/10.1007/s12665-016-5435-6