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Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models

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Abstract

Accurate prediction of water demand in a city is crucial for the management of urban water distribution system. The current study aims to create adequate daily water demand forecasting models for the Canadian metropolis of London utilizing deep learning (DL)-based models. This study explores the potential of two stand-alone DL models for daily water demand modeling using a convolutional neural network (CNN) and the long short-term memory (LSTM) along with their hybrid CNN–LSTM model. Furthermore, a deep learning-based bi-directional LSTM model is introduced with CNN to predict daily water consumption in London as CNN–BiLSTM hybrid model. Daily water consumption data for the years 2009 to 2020 are used for the development and assessment of the predictive models. These stand-alone and hybrid models have been developed with specified input lags of daily water demand and verified for daily water demand prediction. The performance of the developed hybrid models was compared with other well-established DL-based stand-alone models. The model outcomes during the training, validation, and testing phases were assessed using statistical metrics such as the mean absolute error (MAE), Nash–Sutcliffe coefficient (NSE), correlation coefficient (r), Scatter Index (SI), Mean Bias Error (MBE), and Discrepancy Ratio (DR). The stand-alone models captured the observations very well during training and testing which is obvious at 1-day ahead. Moreover, at 7 days and 15 days ahead those models, except the stand-alone CNN, closely reproduced the pattern in daily water demand. Among the hybrid models, the outperforming CNN–BiLSTM model produced 1-day to 15-day multi-step ahead forecasting with performance metrics in the following ranges: MAE = 0.245–2.541 ML/day, NSE = 99.830–84.843% and r = 0.999–0.921 during the testing period. The uncertainty analysis has been performed which advocates the superiority of the CNN–BiLSTM model showing the forecasts bands with 88–90% observations within the 90% confidence interval (CI). Overall, the outcomes supporting the CNN–BiLSTM is to be considered as a promising deep learning procedure for accurate forecasting of urban water demand in any city globally.

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Data Availability

Upon reasonable request, the corresponding author will provide access to all the data and codes that support the findings of this study.

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Acknowledgements

The authors would like to acknowledge Municipal Artificial Intelligence application lab out of the Information Technology Services division, London, Canada for sharing data.

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Authors and Affiliations

  1. Department of Agricultural Engineering, Centurion University, Paralakhemundi, Odisha, India

    Bibhuti Bhusan Sahoo

  2. Toxicology Center, University of Saskatchewan, 44 Campus Drive, Saskatoon, SK, S7N 5B3, Canada

    Banamali Panigrahi

  3. Soil and Water Conservation, Directorate of Soil Conservation and Watershed Development, Bhubaneswar, Odisha, 768004, India

    Trushnamayee Nanda

  4. College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, 389001, India

    Mukesh Kumar Tiwari

  5. Department of Civil Engineering, Centurion University of Technology and Management, Bhubaneswar, Odisha, 761211, India

    Sovan Sankalp

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  1. Bibhuti Bhusan Sahoo
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Correspondence to Banamali Panigrahi.

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Sahoo, B.B., Panigrahi, B., Nanda, T. et al. Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models. SN COMPUT. SCI. 4, 752 (2023). https://doi.org/10.1007/s42979-023-02246-6

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