Ensemble modeling approach for rainfall/groundwater balancing
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Laucelli:2007:JH,
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author = "D. Laucelli and O. Giustolisi and V. Babovic and
M. Keijzer",
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title = "Ensemble modeling approach for rainfall/groundwater
balancing",
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journal = "Journal of Hydroinformatics",
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year = "2007",
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volume = "9",
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number = "2",
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pages = "95--106",
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month = mar,
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publisher = "IWA Publishing",
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keywords = "genetic algorithms, genetic programming, ensemble
modelling, groundwater, hydrology",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/009/0095/0090095.pdf",
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DOI = "doi:10.2166/hydro.2007.102",
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size = "12 pages",
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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.",
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notes = "Piana di Brindisi",
- }
Genetic Programming entries for
Daniele B Laucelli
Orazio Giustolisi
Vladan Babovic
Maarten Keijzer
Citations