Support vector regression for rainfall-runoffmodeling in urban drainage: A comparison with the EPA's storm water management model

Water (Switzerland)

Volumn 8, Issue 3, 2016, Pages

  Granata, Francesco ^a   Gargano, Rudy ^a   de Marinis, Giovanni ^a  

^a UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

Author keywords

Peak discharge; Rainfall runoff; Sewer; Storm Water Management Model; Support Vector regression; SWMM; Urban hydrology

Indexed keywords

CATCHMENTS; MEAN SQUARE ERROR; REGRESSION ANALYSIS; RUNOFF; STORMS; SUPPORT VECTOR MACHINES; VECTORS; WATER MANAGEMENT;

CLASSIFIEDS; PEAK DISCHARGE; PHYSICALLY BASED MODELS; RAINFALL RUNOFF; STORMWATER MANAGEMENT MODEL(SWMM); SUPPORT VECTOR REGRESSIONS; SUPPORT VECTORS MACHINE; URBAN DRAINAGE; URBAN HYDROLOGY;

RAIN;

ALGORITHM; ARTIFICIAL INTELLIGENCE; CATCHMENT; HYDROGRAPH; PEAK DISCHARGE; RAINFALL-RUNOFF MODELING; SEWER NETWORK; SUPPORT VECTOR MACHINE; URBAN AREA; WATER MANAGEMENT;

ITALY;

EID: 84964034032     PISSN: None     EISSN: 20734441     Source Type: Journal    
DOI: 10.3390/w8030069     Document Type: Article

References (28)

1. Butler, D.; Davies, J. Urban Drainage, 3rd ed.; CRC Press: Boca Raton, FL, USA, 2011.
2. Coombes, P.J. Transitioning Drainage into UrbanWater Cycle Management. In Proceedings of the WSUD2015 Conference, Engineers Australia, Sydney, Australia, 2015.
3. Beven, K.J. Rainfall-RunoffModelling: The Primer, 2nd ed.;Wiley-Blackwell: Oxford, UK, 2012.
4. Coombes, P.J.; Babister, M.; McAlister, T. Is the Science and Data underpinning the Rational Method Robust for use in Evolving Urban Catchments. In Proceedings of the 36th Hydrology andWater Resources Symposium, Engineers Australia, Hobart, Australia, 2015.
5. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial Neural Networks in Hydrology. I: Preliminary Concepts. J. Hydrol. Eng. 2000, 5, 115-123.
6. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial Neural Networks in Hydrology. II: Hydrologic Applications. J. Hydrol. Eng. 2000, 5, 124-137.
7. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines; Cambridge University Press: Cambridge, UK, 2000.
8. Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995.
9. Cortes, C.; Vapnik, V. Support vector networks. Mach. Learn. 1995, 20, 273-297.
10. Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A training algorithm for optimal margin classifiers. In Proceedings of the Annual Conference on Computational Learning Theory, Pittsburgh, PA, USA; ACM Press: New York, NY, USA, 1992; pp. 144-152.
11. Vapnik, V.; Golowich, S.; Smola, A. Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems; Mozer, M.C., Jordan, M.I., Petsche, T., Eds.; MIT Press: Cambridge, MA, USA, 1997; pp. 281-287.
12. Dibike, Y.B.; Velickov, S.; Solomatine, D.; Abbott, M.B. Model induction with sup-port vector machines: Introduction and application. J. Comput. Civil Eng. 2001, 15, 208-216.
13. Bray, M.; Han, D. Identification of support vector machines for runoffmodelling. J. Hydroinf. 2004, 6, 265-280.
14. Tripathi, S.H.; Srinivas, V.V.; Nanjundiah, R.S. Downscaling of precipitation for climate change scenarios: A support vector machine approach. J. Hydrol. 2006, 330, 621-640.
15. Chen, H.; Guo, J.; Wei, X. Downscaling GCMs using the smooth support vector machine method to predict daily precipitation in the Hanjiang basin. Adv. Atmos. Sci. 2010, 27, 274-284.
16. Garcìa Nieto, P.J.; Martinez Torres, J.; Araùjo Fernàndez, M.; Ordònez Galàn, C. Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Appl. Math. Model. 2012, 36, 6137-6145.
17. Antonanzas, J.; Urraca, R.; Martinez-de-Pison, F.J.; Antonanzas-Torres, F. Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy Convers. Manag. 2015, 100, 380-390.
18. Yu, P.S.; Chen, S.T.; Chang, I.F. Support vector regression for real-time flood stage forecasting. J. Hydrol. 2006, 328, 704-716.
19. Hosseini, S.M.; Mahjouri, N. Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoffmodeling. Appl. Soft Comput. 2016, 38, 329-345.
20. Raghavendra, S.N.; Deka, P.C. Support vector machine applications in the field of hydrology: A review. Appl. Soft Comput. 2014, 19, 372-386.
21. Chang, C.C.; Lin, C.J. Training v-support vector classifiers: Theory and algorithms. Neural Comput. 2001, 13, 2119-2147.
22. Smola, A.J.; Scholkopf, B. A tutorial on support vector regression. Stat. Comput. 2004, 14, 199-222.
23. Karush, W. Minima of functions of several variables with inequalities as side constraints. Master's Thesis, Department of Mathematics, University of Chicago, Chicago, IL, USA, 1939.
24. Kuhn, H.W.; Tucker, A.W. Nonlinear programming. In Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics; University of California Press: Oakland, CA, USA, 1951; pp. 481-492.
25. Rossman, L.A. Storm Water Management Model User's Manual, Version 5.0; U.S. Environmental Protection Agency: Cincinnati, OH, USA, 2004.
26. Box, M.J. A new method of constrained optimization and comparison with other methods. Comput. J. 1965, 8, 42-52.
27. Barco, J.; Wong, K.M.; Stenstrom, M.K. Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment. J. Hydraul. Eng. 2008, 134, 466-474.
28. Calomino, F.; Paoletti, A. Le Misure di Pioggia e di Portata Nei Bacini Sperimentali Urbani in Italia; CSDU: Milan, Italy, 1994.