Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations
Created by W.Langdon from
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- @Article{Shiri2010,
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author = "Jalal Shiri and Ozgur Kisi",
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title = "Comparison of genetic programming with neuro-fuzzy
systems for predicting short-term water table depth
fluctuations",
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journal = "Computer \& Geosciences",
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volume = "37",
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number = "10",
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pages = "1692--1701",
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month = oct,
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year = "2011",
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ISSN = "0098-3004",
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DOI = "doi:10.1016/j.cageo.2010.11.010",
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URL = "http://www.sciencedirect.com/science/article/B6V7D-51NNPFY-3/2/a9730f48b501cdb19b0197c8197ea42c",
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keywords = "genetic algorithms, genetic programming, Groundwater
depth fluctuation, Neuro-fuzzy, Forecast",
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abstract = "This paper investigates the ability of genetic
programming (GP) and adaptive neuro-fuzzy inference
system (ANFIS) techniques for groundwater depth
forecasting. Five different GP and ANFIS models
comprising various combinations of water table depth
values from two stations, Bondville and Perry, are
developed to forecast one-, two- and three-day ahead
water table depths. The root mean square errors (RMSE),
scatter index (SI), Variance account for (VAF) and
coefficient of determination (R2) statistics are used
for evaluating the accuracy of models. Based on the
comparisons, it was found that the GP and ANFIS models
could be employed successfully in forecasting water
table depth fluctuations. However, GP is superior to
ANFIS in giving explicit expressions for the problem.",
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notes = "See \cite{Beriro2012}",
- }
Genetic Programming entries for
Jalal Shiri
Ozgur Kisi
Citations