Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

Li Mu, Feifei Zheng, Ruoling Tao, Qingzhou Zhang, Zoran Kapelan

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 h. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications.

Original languageEnglish
Article number05020017
Number of pages11
JournalJournal of Water Resources Planning and Management
Volume146
Issue number9
DOIs
Publication statusPublished - 2020

Keywords

  • ARIMA models
  • Data-driven models
  • Long short-term memory
  • Water demand prediction

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