Modeling catchment sediment yield: a genetic programming approach
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- @Article{Garg:2014:NH,
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author = "Vaibhav Garg",
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title = "Modeling catchment sediment yield: a genetic
programming approach",
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journal = "Natural Hazards",
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year = "2014",
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volume = "70",
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number = "1",
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pages = "39--50",
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month = jan,
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email = "vaibhav@iirs.gov.in",
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keywords = "genetic algorithms, genetic programming, Sediment
yield, Modelling and simulation, Evolutionary
technique, Soft computing",
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publisher = "Springer",
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ISSN = "0921-030X",
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language = "English",
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URL = "http://link.springer.com/article/10.1007%2Fs11069-011-0014-3#page-1",
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DOI = "doi:10.1007/s11069-011-0014-3",
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size = "12 pages",
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abstract = "Hydrologic processes are complex, non-linear, and
distributed within a watershed both spatially and
temporally. One such complex pervasive process is soil
erosion. This problem is usually approached directly by
considering the sediment yield. Most of the hydrologic
models developed and used earlier in sediment yield
modelling were lumped and had no provision for
including spatial and temporal variability of the
terrain and climate attributes. This study investigates
the suitability of a recent evolutionary technique,
genetic programming (GP), in estimating sediment yield
considering various meteorological and geographic
features of a basin. The Arno River basin in Italy,
which is prone to frequent floods, has been chosen as
case study to demonstrate the GP approach. The results
of the present study show that GP can efficiently
capture the trend of sediment yield, even with a small
set of data. The major advantage of the GP analysis is
that it generates simple parsimonious expression
offering some possible interpretations to the
underlying process.",
- }
Genetic Programming entries for
Vaibhav Garg
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