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Author:

Liu, Zilong (Liu, Zilong.) | Zhou, Yuwen (Zhou, Yuwen.) (Scholars:周玉文) | Wang, Qiang (Wang, Qiang.) | Ye, Wanlu (Ye, Wanlu.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

A SWMM (Storm water management model)-based method for the adjustment of rain-off model parameters is proposed to realize the equivalent design conditions in pipe network design calibration with hydraulic model. The method proceeds from the basic principle of Rational Method, and takes the assumption of runoff curves with time as the basis. By simulating the runoff catchment through constant rainfall pattern, the principle of equivalent can be judged by whether the features of the simulated runoff curves matched those of the assumption with Rational Method. Case study has verified the feasibility of the proposed method. Meanwhile, to simplify the manual operation in parameters adjustment, an improved particle swarm optimization (PSO) has been put forward to complete the process automatically. ©, 2015, Harbin Institute of Technology. All right reserved.

Keyword:

Particle swarm optimization (PSO) Runoff Rain Water management Calibration Hydraulic models Catchments

Author Community:

  • [ 1 ] [Liu, Zilong]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zhou, Yuwen]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Wang, Qiang]Beijing Municipal Institute of City Planning & Design, Beijing; 100037, China
  • [ 4 ] [Ye, Wanlu]Beijing Municipal Institute of City Planning & Design, Beijing; 100037, China

Reprint Author's Address:

  • [liu, zilong]college of architecture and civil engineering, beijing university of technology, beijing; 100124, china

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Source :

Journal of Harbin Institute of Technology

ISSN: 0367-6234

Year: 2015

Issue: 8

Volume: 47

Page: 92-95 and 112

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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