Modeling rainfall-runoff process using soft computing techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Kisi:2013:CG,
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author = "Ozgur Kisi and Jalal Shiri and Mustafa Tombul",
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title = "Modeling rainfall-runoff process using soft computing
techniques",
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journal = "Computer \& Geosciences",
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volume = "51",
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pages = "108--117",
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year = "2013",
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month = feb,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Rainfall-runoff process, Neural
networks, Neuro-fuzzy system",
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ISSN = "0098-3004",
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DOI = "doi:10.1016/j.cageo.2012.07.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S0098300412002257",
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abstract = "Rainfall-runoff process was modelled for a small
catchment in Turkey, using 4 years (1987-1991) of
measurements of independent variables of rainfall and
runoff values. The models used in the study were
Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy
Inference System (ANFIS) and Gene Expression
Programming (GEP) which are Artificial Intelligence
(AI) approaches. The applied models were trained and
tested using various combinations of the independent
variables. The goodness of fit for the model was
evaluated in terms of the coefficient of determination
(R2), root mean square error (RMSE), mean absolute
error (MAE), coefficient of efficiency (CE) and scatter
index (SI). A comparison was also made between these
models and traditional Multi Linear Regression (MLR)
model. The study provides evidence that GEP (with
RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is
capable of modelling rainfall-runoff process and is a
viable alternative to other applied artificial
intelligence and MLR time-series methods.",
- }
Genetic Programming entries for
Ozgur Kisi
Jalal Shiri
Mustafa Tombul
Citations