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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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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