publication . Other literature type . Article . 2018

A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

Patricia Jimeno Sáez; Julio Pérez-Sánchez; David Pulido-Velazquez;
Open Access
  • Published: 11 Feb 2018
  • Publisher: MDPI AG
Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs). Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This s...
free text keywords: Geography, Planning and Development, Aquatic Science, Biochemistry, Water Science and Technology, Soil and Water Assessment Tool (SWAT), Artificial Neural Network (ANN), data imputation, runoff simulation, hydrologic modelling, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
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Article . 2018
Provider: Crossref
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Article . 2018
66 references, page 1 of 5

runoff simulation in Balkhichai river watershed, Iran. Am. J. Clim. Chang. 2015, 4, 203-216. [CrossRef]

Performance Evaluation of Hydrological Models. J. Hydrol. 2014, 510, 447-458. [CrossRef]

2000, 235, 276-288. [CrossRef]

2016, 533, 141-151. [CrossRef]

2017, 3, 635-645. [CrossRef]

part I: Model development. J. Am. Water Resour. Assoc. 1998, 34, 73-89. [CrossRef]

of Different Spatial Resolutions. Water 2017, 9, 54. [CrossRef]

8. Abbaspour, K.C.; Rouholahnejad, E.; Vaghefi, S.; Srinivasan, R.; Yang, H.; Klove, B. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. J. Hydrol. 2015, 524, 733-752. [CrossRef] [OpenAIRE]

9. Olyaie, E.; Banejad, H.; Chau, K.W.; Melesse, A.M. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States. Environ. Monit. Assess. 2015, 187, 189. [CrossRef] [PubMed] [OpenAIRE]

10. Gholami, V.; Chau, K.W.; Fadaee, F.; Torkaman, J.; Ghaffari, A. Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. J. Hydrol. 2015, 529, 1060-1069. [CrossRef] [OpenAIRE]

11. Chen, X.Y.; Chau, K.W. A hybrid double feedforward neural network for suspended sediment load estimation. Water Resour. Manag. 2016, 30, 2179-2194. [CrossRef]

12. Jimeno-Sáez, P.; Senent-Aparicio, J.; Pérez-Sánchez, J.; Pulido-Velazquez, D.; Cecilia, J.M. Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water 2017, 9, 347. [CrossRef] [OpenAIRE]

13. Panu, U.S.; Khalil, M.; Elshorbagy, A. Streamflow data infilling techniques based on concepts of groups and neural networks. In Artificial Neural Networks in Hydrology; Govindaraju, R.S., Rao, A.R., Eds.; Water Science and Technology Library; Springer: Dordrecht, The Netherlands, 2000; Volume 36, pp. 235-258. ISBN 978-94-015-9341-0. [CrossRef]

14. Elsholberg, A.; Simonovic, S.P.; Panu, U.S. Estimation of missing streamflow data using principles of chaos theory. J. Hydrol. 2002, 255, 123-133. [CrossRef] [OpenAIRE]

15. Minns, W.; Hall, M.J. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 1996, 41, 399-417. [CrossRef]

66 references, page 1 of 5
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