Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Mohsen Nasseri and Ali Moeini and Massoud Tabesh",
  title =        "Forecasting monthly urban water demand using Extended
                 Kalman Filter and Genetic Programming",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "6",
  pages =        "7387--7395",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2010.12.087",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Forecasting,
                 Monthly water demand, Extended Kalman Filter (EKF),
                 Data assimilation",
  abstract =     "In this paper, a hybrid model which combines Extended
                 Kalman Filter (EKF) and Genetic Programming (GP) for
                 forecasting of water demand in Tehran is developed. The
                 initial goal of the current work is forecasting monthly
                 water demand using GP for achieving an explicit optimum
                 formula. In the proposed model, the EKF is applied to
                 infer latent variables in order to make a forecasting
                 based on GP results of water demand. The available
                 dataset includes monthly water consumption of Tehran,
                 the capital of Iran, from 1992 to 2002. Five best
                 formulae based on GP results on this dataset are
                 presented. In these models, the first five to three
                 lags of observed water demand are used as probable and
                 independent inputs. For each model, sensitivity of the
                 results for each input is measured mathematically. A
                 model with the most compatibility of the computed
                 versus the observed water demand is used for filtering
                 based on EKF method. Results of GP and hybrid models of
                 EKFGP demonstrate the visible effect of observation
                 precision on water demand prediction. These results can
                 help decision makers of water resources to reduce their
                 risks of on line water demand forecasting and optimal
                 operation of urban water systems.",

Genetic Programming entries for Mohsen Nasseri Ali Moeini Massoud Tabesh