Predicting Baseflow Using Genetic Programing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Meshgi:2014:HIC,
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author = "Ali Meshgi and Petra Schmitter and Vladan Babovic and
Ting Fong May Chui",
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title = "Predicting Baseflow Using Genetic Programing",
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booktitle = "11th International Conference on Hydroinformatics",
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year = "2014",
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address = "New York, USA",
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month = aug # " 17-21",
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organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
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keywords = "genetic algorithms, genetic programming, Baseflow,
Recursive Digital Filters, Numerical modeling",
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isbn13 = "978-0-692-28129-1",
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URL = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1589/1153.pdf",
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size = "8 pages",
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abstract = "Developing reliable methods to estimate baseflow has
been a subject of research interest over the past
decades due to its importance in catchment response and
sustainable watershed management (e.g. ground water
recharge vs. extraction). Limitations and complexities
of existing methods have been addressed by a number of
researchers. For instance, physically based numerical
models are complex, requiring substantial computational
time and data which may not be always available.
Artificial Intelligence (AI) tools such as Genetic
Programming (GP) have been used widely to reduce the
challenges associated with complex hydrological systems
without losing the physical meanings. However, up to
date, in the absence of complex numerical models,
baseflow is frequently estimated using statistically
derived empirical equations without significant
physical insights. This study investigates the
capability of GP in estimating baseflow for a small
intensively monitored semi-urban catchment (8.5 ha)
located in Singapore. The validated GP model for
Singapore is tested on a larger vegetation-dominated
basin located in the USA (24 km2). For each study case,
the baseflow predictions from the established GP model
were compared with baseflow estimates obtained through
the use of the Recursive Digital Filters (RDFs) method
using the available discharge time series. The
Nash-Sutcliffe efficiency of 0.94 and 0.91 are found
with comparing the baseflow estimated by GP and RDFs in
the first and second study sites, respectively. These
results indicate that GP is an effective tool in
determining baseflow. Overall, this study proposes a
new approach which can predict the baseflow with only
information on three parameters including minimum
baseflow in dry period, area of the catchment and
groundwater table.",
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notes = "Broken June 2021 http://www.hic2014.org/xmlui/",
- }
Genetic Programming entries for
Ali Meshgi
Petra Schmitter
Vladan Babovic
Ting Fong May Chui
Citations