A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Highlights

● A novel deep learning framework for short-term water demand forecasting.

● Model prediction accuracy outperforms other traditional deep learning models.

● Wavelet multi-resolution analysis automatically extracts key water demand features.

● An analysis is performed to explain the improved mechanism of the proposed method.

Abstract

Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.

Graphical abstract

Keywords

Short-term water demand forecasting / Long-short term memory neural network / Convolutional Neural Network / Wavelet multi-resolution analysis / Data-driven models

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 51978494), and the Science and Technology Innovation Program Project of Shanghai City Investment Co., Ltd. (No. CTKY-ZDXM-2020-012).

Data Accessibility Statement

The data supporting the findings of this study are available within the article and its supplementary materials. The code not available due to intellectual property rights of cooperative institute restrictions.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1622-3 and is accessible for authorized users.

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