Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

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@Article{Hashim:2016:AR,
  author =       "Roslan Hashim and Chandrabhushan Roy and 
                 Shervin Motamedi and Shahaboddin Shamshirband and 
                 Dalibor Petkovic and Milan Gocic and Siew Cheng Lee",
  title =        "Selection of meteorological parameters affecting
                 rainfall estimation using neuro-fuzzy computing
                 methodology",
  journal =      "Atmospheric Research",
  volume =       "171",
  pages =        "21--30",
  year =         "2016",
  ISSN =         "0169-8095",
  DOI =          "doi:10.1016/j.atmosres.2015.12.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169809515003920",
  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.",
  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