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Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes

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A Correction to this article was published on 22 December 2023

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Abstract

Efficient and optimal management of urban water distribution networks needs to forecast the amount of short-term water demand for a day and night at hourly intervals. Water demand has a time series nature and a pattern with a complex structure, which is influenced by many factors. Deep neural networks (DNNs) can be suitable for extracting this pattern. In this study, a One-Dimensional convolutional neural network (1D CNN) is implemented for the short-term forecast of urban water. Next, the obtained outputs are compared with other deep learning models, including deep feedforward neural network (DFNN), simple recurrent neural network (Simple RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) neural networks. The results show that the 1D CNN with a Mean Absolute Percentage Error (MAPE) of 3.52% is superior to other DNNs. This issue and the short time required to train the 1D CNN model compared to other models make this model superior. Deep learning models were implemented in the TensorFlow software platform and Keras library in Python. In this study, the Rolling Cross-Validation technique was used to evaluate and adjust the hyperparameters of deep learning models.

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Data availability

The data that support the findings of this study are available from Water and Sewage Engineering Company of Shiraz city in Iran. Still, restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

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Acknowledgements

In the end, Water and Wastewater Engineering Company of Shiraz city is appreciated for its cooperation and for providing water demand data.

Funding

The authors have no financial or proprietary interests in any material discussed in this article.

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Authors and Affiliations

  1. Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Golestan Blvd, Ahvaz, IR, Iran

    Hossein Namdari, Ali Haghighi & Seyed Mohammad Ashrafi

Authors
  1. Hossein Namdari
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  2. Ali Haghighi
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  3. Seyed Mohammad Ashrafi
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Hossein Namdari, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hossein Namdari.

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Namdari, H., Haghighi, A. & Ashrafi, S.M. Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes. Stoch Environ Res Risk Assess (2023). https://doi.org/10.1007/s00477-023-02565-3

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