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

doi:10.1007/s11783-023-1622-3

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (2) : 22 https://doi.org/10.1007/s11783-023-1622-3

RESEARCH ARTICLE

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

Zhengheng Pu^1,², Jieru Yan^1,², Lei Chen^1,², Zhirong Li^1,², Wenchong Tian^1,², Tao Tao^1,², Kunlun Xin^1,²(

)

¹. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
². Smart Water Joint Innovation RD Center, Tongji University, Shanghai 200092, China

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Abstract

● 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.

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.

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

Corresponding Author(s): Kunlun Xin

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Issue Date: 08 September 2022

Cite this article:

Zhengheng Pu,Jieru Yan,Lei Chen, et al. A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting[J]. Front. Environ. Sci. Eng., 2023, 17(2): 22.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1622-3
https://academic.hep.com.cn/fese/EN/Y2023/V17/I2/22

Fig.1 Schematic diagram of 1D-CNN.

Fig.2 Structure of a long-short term memory (LSTM) network cell.

Fig.3 A flowchart of Wavelet-CNN-LSTM.

Fig.4 Water demand distribution on a daily scale (a) Average water demand variation pattern (b) variation interval of water demand in different time periods.

Tab.1 Model structure and model parameters

Tab.2

Tab.3 The hyper-parameter search range and optimal configuration of models

Tab.4 Average performance indicators of prediction models evaluated from 20 testing datasets

Fig.5 Box plots of the predication performance indicators for the 15-min prediction (evaluated from 20 randomly selected test sets).

Fig.6 Box plots of the predication performance indicators for the 1-h prediction (evaluated from 20 randomly selected test sets).

Fig.7 1-h predictions versus observations for the five models.

Fig.8 15-min predictions versus observations for the five models.

Fig.9 Sub series model performance at different scales (a) cA3 (b) cD3 (c) cD2 (d) cD1.

Fig.10 Predictability indicators and performance indicators distribution at different scales evaluated from the 20 randomly selected test sets

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