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Water 2016, 8(3), 69; doi:10.3390/w8030069

Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA’s Storm Water Management Model

Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via G. Di Biasio 43, 03043 Cassino (FR), Italy
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Academic Editor: Peter Coombes
Received: 23 November 2015 / Revised: 14 February 2016 / Accepted: 15 February 2016 / Published: 24 February 2016
(This article belongs to the Special Issue Urban Water Challenges)
View Full-Text   |   Download PDF [2001 KB, uploaded 24 February 2016]   |  

Abstract

Rainfall-runoff models can be classified into three types: physically based models, conceptual models, and empirical models. In this latter class of models, the catchment is considered as a black box, without any reference to the internal processes that control the transformation of rainfall to runoff. In recent years, some models derived from studies on artificial intelligence have found increasing use. Among these, particular attention should be paid to Support Vector Machines (SVMs). This paper shows a comparative study of rainfall-runoff modeling between a SVM-based approach and the EPA’s Storm Water Management Model (SWMM). The SVM is applied in the variant called Support Vector regression (SVR). Two different experimental basins located in the north of Italy have been considered as case studies. Two criteria have been chosen to assess the consistency between the recorded and predicted flow rates: the root-mean square error (RMSE) and the coefficient of determination. The two models showed comparable performance. In particular, both models can properly model the hydrograph shape, the time to peak and the total runoff. The SVR algorithm tends to underestimate the peak discharge, while SWMM tends to overestimate it. SVR shows great potential for applications in the field of urban hydrology, but currently it also has significant limitations regarding the model calibration. View Full-Text
Keywords: rainfall-runoff; peak discharge; Support Vector regression; Storm Water Management Model; SWMM; sewer; urban hydrology rainfall-runoff; peak discharge; Support Vector regression; Storm Water Management Model; SWMM; sewer; urban hydrology
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Granata, F.; Gargano, R.; de Marinis, G. Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA’s Storm Water Management Model. Water 2016, 8, 69.

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