Ensemble modeling approach for rainfall/groundwater balancing

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

@Article{Laucelli:2007:JH,

• author = "D. Laucelli and O. Giustolisi and V. Babovic and M. Keijzer",
• title = "Ensemble modeling approach for rainfall/groundwater balancing",
• journal = "Journal of Hydroinformatics",
• year = "2007",
• volume = "9",
• number = "2",
• pages = "95--106",
• month = mar,
• publisher = "IWA Publishing",
• keywords = "genetic algorithms, genetic programming, ensemble modelling, groundwater, hydrology",
• ISSN = "1464-7141",
• URL = "http://www.iwaponline.com/jh/009/0095/0090095.pdf",
• DOI = "doi:10.2166/hydro.2007.102",
• size = "12 pages",
• abstract = "This paper introduces an application of machine learning, on real data. It deals with Ensemble Modelling, a simple averaging method for obtaining more reliable approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance, have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic Programming parameter settings on the model's performance.",
• notes = "Piana di Brindisi",

}

Genetic Programming entries for Daniele B Laucelli Orazio Giustolisi Vladan Babovic Maarten Keijzer

Citations