6. Conclusion

Over the past decades, hydrologists have paid attention to data-driven modeling techniques. City governments and WDS operators are always looking for an accurate estimation of water demand values not only for future but also focusing on probable failures like peak consumption and pressure values to manage the WDS pipelines. Therefore, the wide variety of modeling techniques such as artificial and evolutionary simulation methods are proposed by researchers. This chapter investigated the performance of three techniques (ANN, GEP, and MLR) in forecasting short-term water demand of Kelowna City (BC, Canada). About 6 years daily dataset was employed for training and testing the models. First 5 years were considered to train the model and the last year as the test period. All three techniques performed considerably accurate, while the focus of this chapter was on improving the accuracy of the models for the same dataset. Firstly, the model was calibrated by different input combination with 1 day lag time. Then, models were calibrated by the lag time of the data set (83-day) which was calculated by ACF method. WDT was combined with the models to capture multi-scale features of the signals by decomposing observed demand values into sub-series. Five WDT functions (haar, Db2, db4, Sym2, and sym4) were employed to decompose the dataset. The results were then compared with the MLR, ANN, and GEP when no pre-processing (PSR, WDT) was applied. The research results were accurate than PSR. WDT have also improved the accuracy of models with PSR and without PSR. However, the impact of wavelet on the models with PSR was not as considerable as without PSR. The lowest error was reported by W-ANN among all alternative models in this chapter. Regarding the improvement of all models combining WDT and PSR, it is recommended to use the method in modeling and forecasting issues, especially about the dataset that the peak points are very critical in the case. The inherent behavior of dataset (deterministic or stochastic) can affect the performance of the pre-processing methods. Therefore, behavior of datasets should be investigated before deciding to combine any pre-process methods.

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