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A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Author

Listed:
  • Salah L. Zubaidi

    (University of Wasit)

  • Sandra Ortega-Martorell

    (Liverpool John Moores University)

  • Patryk Kot

    (Liverpool John Moores University)

  • Rafid M. Alkhaddar

    (Liverpool John Moores University)

  • Mawada Abdellatif

    (Liverpool John Moores University)

  • Sadik K. Gharghan

    (Electrical Engineering Technical College Middle Technical University (MTU))

  • Maytham S. Ahmed

    (General Directorate of Electrical Energy Production-Basrah, Ministry of Electricity)

  • Khalid Hashim

    (Liverpool John Moores University)

Abstract

The accurate forecast of water demand is challenging for water utilities, specifically when considering the implications of climate change. As such, this is the first study that focuses on finding associations between monthly climate factors and municipal water consumption, using baseline data collected between 1980 and 2010. The aim of the study was to investigate the reliability and capability of a combination of techniques, including Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANNs), to accurately predict long-term, monthly water demands. The principal findings of this research are as follows: a) SSA is a powerful method when applied to remove the impact of socio-economic variables and noise, and to determine a stochastic signal for long-term water consumption time series; b) ANN performed better when optimised using the Lightning Search Algorithm (LSA-ANN) compared with other approaches used in previous studies, i.e. hybrid Particle Swarm Optimisation (PSO-ANN) and Gravitational Search Algorithm (GSA-ANN); c) the proposed LSA-ANN methodology was able to produce a highly accurate and robust model of water demand, achieving a correlation coefficient of 0.96 between observed and predicted water demand when using a validation dataset, and a very small root mean square error of 0.025.

Suggested Citation

  • Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:3:d:10.1007_s11269-020-02500-z
    DOI: 10.1007/s11269-020-02500-z
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    References listed on IDEAS

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    1. Sarita Gajbhiye Meshram & M. A. Ghorbani & Ravinesh C. Deo & Mahsa Hasanpour Kashani & Chandrashekhar Meshram & Vahid Karimi, 2019. "New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2335-2356, May.
    2. Ammar Hussein Mutlag & Azah Mohamed & Hussain Shareef, 2016. "A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board," Energies, MDPI, vol. 9(3), pages 1-32, February.
    3. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    4. Hojat Karami & Saeed Farzin & Aylin Jahangiri & Mohammad Ehteram & Ozgur Kisi & Ahmed El-Shafie, 2019. "Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2741-2760, June.
    5. E. Pacchin & F. Gagliardi & S. Alvisi & M. Franchini, 2019. "A Comparison of Short-Term Water Demand Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1481-1497, March.
    6. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.
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    Cited by:

    1. Diana Fiorillo & Zoran Kapelan & Maria Xenochristou & Francesco De Paola & Maurizio Giugni, 2021. "Assessing the Impact of Climate Change on Future Water Demand using Weather Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1449-1462, March.
    2. Zachary Darby & Neelam Chandra Poudyal & Adam Frakes & Omkar Joshi, 2021. "Economic Analysis of Recreation Access at a Lake Facing Water Crisis due to Municipal Water Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2909-2920, July.
    3. Taís Maria Nunes Carvalho & Francisco Souza Filho, 2021. "Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3431-3445, August.

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