A gene-wavelet model for long lead time drought forecasting

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@Article{DanandehMehr:2014:JH,
  author =       "Ali Danandeh Mehr and Ercan Kahya and Mehmet Ozger",
  title =        "A gene-wavelet model for long lead time drought
                 forecasting",
  journal =      "Journal of Hydrology",
  volume =       "517",
  pages =        "691--699",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Drought
                 forecasting, Linear genetic programing, Wavelet
                 transform, El Nino-Southern Oscillation, Palmer's
                 modified drought index, Hydrologic models",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2014.06.012",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169414004727",
  abstract =     "Summary Drought forecasting is an essential ingredient
                 for drought risk and sustainable water resources
                 management. Due to increasing water demand and looming
                 climate change, precise drought forecasting models have
                 recently been receiving much attention. Beginning with
                 a brief discussion of different drought forecasting
                 models, this study presents a new hybrid gene-wavelet
                 model, namely wavelet-linear genetic programing (WLGP),
                 for long lead-time drought forecasting. The idea of
                 WLGP is to detect and optimise the number of
                 significant spectral bands of predictors in order to
                 forecast the original predict and (drought index)
                 directly. Using the observed El Nno-Southern
                 Oscillation indicator (NINO 3.4 index) and Palmer's
                 modified drought index (PMDI) as predictors and future
                 PMDI as predictand, we proposed the WLGP model to
                 forecast drought conditions in the State of Texas with
                 3, 6, and 12-month lead times. We compared the
                 efficiency of the model with those of a classic linear
                 genetic programing model developed in this study, a
                 neuro-wavelet (WANN), and a fuzzy-wavelet (WFL) drought
                 forecasting models formerly presented in the relevant
                 literature. Our results demonstrated that the classic
                 linear genetic programing model is unable to learn the
                 non-linearity of drought phenomenon in the lead times
                 longer than 3 months; however, the WLGP can be
                 effectively used to forecast drought conditions having
                 3, 6, and 12-month lead times. Genetic-based
                 sensitivity analysis among the input spectral bands
                 showed that NINO 3.4 index has strong potential effect
                 in drought forecasting of the study area with
                 6-12-month lead times.",
}

Genetic Programming entries for Ali Danandeh Mehr Ercan Kahya Mehmet Ozger

Citations