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Open AccessArticle

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Faculty of Engineering and Natural Sciences, Konya Technical University, Konya 42075, Turkey
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Water 2019, 11(1), 147; https://doi.org/10.3390/w11010147
Received: 30 October 2018 / Revised: 10 January 2019 / Accepted: 11 January 2019 / Published: 16 January 2019
(This article belongs to the Special Issue Hydrologic Modelling for Water Resources and River Basin Management)
Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R2) = 0.787 and Nash–Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models. View Full-Text
Keywords: SWAT; SUFI-2; RBNN; SVM; hydrological modelling SWAT; SUFI-2; RBNN; SVM; hydrological modelling
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Koycegiz, C.; Buyukyildiz, M. Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin. Water 2019, 11, 147.

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