Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology
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- @Article{Hashim:2016:AR,
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author = "Roslan Hashim and Chandrabhushan Roy and
Shervin Motamedi and Shahaboddin Shamshirband and
Dalibor Petkovic and Milan Gocic and Siew Cheng Lee",
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title = "Selection of meteorological parameters affecting
rainfall estimation using neuro-fuzzy computing
methodology",
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journal = "Atmospheric Research",
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volume = "171",
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pages = "21--30",
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year = "2016",
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ISSN = "0169-8095",
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DOI = "doi:10.1016/j.atmosres.2015.12.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169809515003920",
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abstract = "Rainfall is a complex atmospheric process that varies
over time and space. Researchers have used various
empirical and numerical methods to enhance estimation
of rainfall intensity. We developed a novel prediction
model in this study, with the emphasis on accuracy to
identify the most significant meteorological parameters
having effect on rainfall. For this, we used five input
parameters: wet day frequency (dwet), vapour pressure (
e - a ), and maximum and minimum air temperatures (Tmax
and Tmin) as well as cloud cover (cc). The data were
obtained from the Indian Meteorological Department for
the Patna city, Bihar, India. Further, a type of
soft-computing method, known as the
adaptive-neuro-fuzzy inference system (ANFIS), was
applied to the available data. In this respect, the
observation data from 1901 to 2000 were employed for
testing, validating, and estimating monthly rainfall
via the simulated model. In addition, the ANFIS process
for variable selection was implemented to detect the
predominant variables affecting the rainfall
prediction. Finally, the performance of the model was
compared to other soft-computing approaches, including
the artificial neural network (ANN), support vector
machine (SVM), extreme learning machine (ELM), and
genetic programming (GP). The results revealed that
ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572,
0.9764, 0.9525, and 0.9526, respectively. Therefore, we
conclude that the ANFIS is the best method among all to
predict monthly rainfall. Moreover, dwet was found to
be the most influential parameter for rainfall
prediction, and the best predictor of accuracy. This
study also identified sets of two and three
meteorological parameters that show the best
predictions.",
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keywords = "genetic algorithms, genetic programming, Rainfall,
Forecasting, Meteorological data, Anfis, Variable
selection",
- }
Genetic Programming entries for
Roslan Hashim
Chandrabhushan Roy
Shervin Motamedi
Shahaboddin Shamshirband
Dalibor Petkovic
Milan Gocic
Siew Cheng Lee
Citations