Genetic Programming Based Approach Towards Understanding the Dynamics of Urban Rainfall-runoff Process

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Chadalawada:2016:PE,
  author =       "Jayashree Chadalawada and Vojtech Havlicek and 
                 Vladan Babovic",
  title =        "Genetic Programming Based Approach Towards
                 Understanding the Dynamics of Urban Rainfall-runoff
                 Process",
  journal =      "Procedia Engineering",
  volume =       "154",
  pages =        "1093--1102",
  year =         "2016",
  note =         "12th International Conference on Hydroinformatics (HIC
                 2016) - Smart Water for the Future",
  ISSN =         "1877-7058",
  DOI =          "doi:10.1016/j.proeng.2016.07.601",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1877705816319907",
  abstract =     "Genetic Programming (GP) is an evolutionary-algorithm
                 based methodology that is the best suited to model
                 non-linear dynamic systems. The potential of GP has not
                 been exploited to the fullest extent in the field of
                 hydrology to understand the complex dynamics involved.
                 The state of the art applications of GP in hydrological
                 modelling involve the use of GP as a short-term
                 prediction and forecast tool rather than as a framework
                 for the development of a better model that can handle
                 current challenges. In today's scenario, with
                 increasing monitoring programmes and computational
                 power, the techniques like GP can be employed for the
                 development and evaluation of hydrological models,
                 balancing, prior information, model complexity, and
                 parameter and output uncertainty. In this study, GP
                 based data driven model in a single and multi-objective
                 framework is trained to capture the dynamics of the
                 urban rainfall-runoff process using a series of tanks,
                 where each tank is a storage unit in a watershed that
                 corresponds to varying depths below the surface. The
                 hydro-meteorological data employed in this study
                 belongs to the Kent Ridge catchment of National
                 University Singapore, a small urban catchment (8.5
                 hectares) that receives a mean annual rainfall of 2500
                 mm and consists of all the major land uses of
                 Singapore.",
  keywords =     "genetic algorithms, genetic programming,
                 Multi-objective optimization, System Identification,
                 Data driven modelling in Hydrology, Urban
                 Rainfall-Runoff modelling",
}

Genetic Programming entries for Jayashree Chadalawada Vojtech Havlicek Vladan Babovic

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