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

Authors:
Mohsen Nasseri;Ali Moeini;Massoud Tabesh
Affiliations:
School of Civil Engineering, University of Tehran, Iran;Department of Algorithms and Computations, University of Tehran, Iran;Center of Excellence for Engineering and Management of Infrastructure, School of Civil Engineering, University of Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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 formulas 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 online water demand forecasting and optimal operation of urban water systems.