Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data

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@Article{Kisi:2015:CEA,
  author =       "Ozgur Kisi and Hadi Sanikhani and 
                 Mohammad Zounemat-Kermani and Faegheh Niazi",
  title =        "Long-term monthly evapotranspiration modeling by
                 several data-driven methods without climatic data",
  journal =      "Computers and Electronics in Agriculture",
  volume =       "115",
  pages =        "66--77",
  year =         "2015",
  ISSN =         "0168-1699",
  DOI =          "doi:10.1016/j.compag.2015.04.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0168169915001222",
  abstract =     "In this study, the ability of four different
                 data-driven methods, multilayer perceptron artificial
                 neural networks (ANN), adaptive neuro-fuzzy inference
                 system (ANFIS) with grid partition (GP), ANFIS with
                 subtractive clustering (SC) and gene expression
                 programming (GEP), was investigated in predicting
                 long-term monthly reference evapotranspiration (ET0) by
                 using data from 50 stations in Iran. The periodicity
                 component, station latitude, longitude and altitude
                 values were used as inputs to the applied models to
                 predict the long-term monthly ET0 values. The overall
                 accuracies of the multilayer perceptron ANN, ANFIS-GP
                 and ANFIS-SC models were found to be similar to each
                 other. The GEP model provided the worst estimates. The
                 maximum determination coefficient (R2) values were
                 found to be 0.997, 998 and 0.994 for the ANN, ANFIS-GP
                 and ANFIS-SC models in Karaj station, respectively. The
                 highest R2 value (0.978) of GEP model was found for the
                 Qom station. The minimum R2 values were respectively
                 found as 0.959 and 0.935 for the ANN and ANFIS-GP
                 models in Bandar Abbas station while the ANFIS-SC and
                 GEP models gave the minimum R2 values of 0.937 and
                 0.677 in the Tabriz and Kerman stations, respectively.
                 The results indicated that the long-term monthly
                 reference evapotranspiration of any site can be
                 successfully estimated by data-driven methods applied
                 in this study without climatic measurements. The
                 interpolated maps of ET0 were also obtained by using
                 the optimal ANFIS-GP model and evaluated in the study.
                 The ET0 maps showed that the highest amounts of
                 reference evapotranspiration occurred in the southern
                 and especially south eastern parts of the Iran.",
  keywords =     "genetic algorithms, genetic programming, Neural
                 networks, Adaptive neuro-fuzzy, Geographical inputs,
                 Reference evapotranspiration",
}

Genetic Programming entries for Ozgur Kisi Hadi Sanikhani Mohammad Zounemat-Kermani Faegheh Niazi

Citations