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

@article{Nasseri2011ForecastingMU,
  title={Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming},
  author={Mohsen Nasseri and Ali Moeini and Massoud Tabesh},
  journal={Expert Syst. Appl.},
  year={2011},
  volume={38},
  pages={7387-7395}
}
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… CONTINUE READING
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