An empirical method for approximating stream baseflow time series using groundwater table fluctuations
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gp-bibliography.bib Revision:1.8098
- @Article{Meshgi:2014:JH,
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author = "Ali Meshgi and Petra Schmitter and Vladan Babovic and
Ting Fong May Chui",
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title = "An empirical method for approximating stream baseflow
time series using groundwater table fluctuations",
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journal = "Journal of Hydrology",
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volume = "519, Part A",
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pages = "1031--1041",
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year = "2014",
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keywords = "genetic algorithms, genetic programming, Baseflow,
Empirical equation, Numerical modelling",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2014.08.033",
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URL = "http://www.sciencedirect.com/science/article/pii/S002216941400643X",
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abstract = "Summary Developing reliable methods to estimate stream
base flow has been a subject of interest due to its
importance in catchment response and sustainable
watershed management. However, to date, in the absence
of complex numerical models, base-flow is most commonly
estimated using statistically derived empirical
approaches that do not directly incorporate
physically-meaningful information. On the other hand,
Artificial Intelligence (AI) tools such as Genetic
Programming (GP) offer unique capabilities to reduce
the complexities of hydrological systems without losing
relevant physical information. This study presents a
simple-to-use empirical equation to estimate baseflow
time series using GP so that minimal data is required
and physical information is preserved. A groundwater
numerical model was first adopted to simulate baseflow
for a small semi-urban catchment (0.043 km2) located in
Singapore. GP was then used to derive an empirical
equation relating baseflow time series to time series
of groundwater table fluctuations, which are relatively
easily measured and are physically related to baseflow
generation. The equation was then generalised for
approximating baseflow in other catchments and
validated for a larger vegetation-dominated basin
located in the US (24 km2). Overall, this study used GP
to propose a simple-to-use equation to predict baseflow
time series based on only three parameters: minimum
daily baseflow of the entire period, area of the
catchment and groundwater table fluctuations. It serves
as an alternative approach for baseflow estimation in
un-gauged systems when only groundwater table and soil
information is available, and is thus complementary to
other methods that require discharge measurements.",
- }
Genetic Programming entries for
Ali Meshgi
Petra Schmitter
Vladan Babovic
Ting Fong May Chui
Citations