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An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

Author

Listed:
  • Kanhua Yu

    (Department of Urban and Rural Planning, Academy of Architecture, Chang’an University, Xi’an 710061, China)

  • Lili Liu

    (Department of Urban and Rural Planning, Academy of Architecture, Chang’an University, Xi’an 710061, China)

  • Zhe Chen

    (Department of Urban and Rural Planning, Academy of Architecture, Chang’an University, Xi’an 710061, China)

Abstract

A slime mould algorithm (SMA) is a new meta-heuristic algorithm, which can be widely used in practical engineering problems. In this paper, an improved slime mould algorithm (ESMA) is proposed to estimate the water demand of Nanchang City. Firstly, the opposition-based learning strategy and elite chaotic searching strategy are used to improve the SMA. By comparing the ESMA with other intelligent optimization algorithms in 23 benchmark test functions, it is verified that the ESMA has the advantages of fast convergence, high convergence precision, and strong robustness. Secondly, based on the data of historical water consumption and local economic structure of Nanchang, four estimation models, including linear, exponential, logarithmic, and hybrid, are established. The experiment takes the water consumption of Nanchang City from 2004 to 2019 as an example to analyze, and the estimation models are optimized using the ESMA to determine the model parameters, then the estimation models are tested. The simulation results show that all four models can obtain better prediction accuracy, and the proposed ESMA has the best effect on the hybrid prediction model, and the prediction accuracy is up to 97.705%. Finally, the water consumption of Nanchang in 2020–2024 is forecasted.

Suggested Citation

  • Kanhua Yu & Lili Liu & Zhe Chen, 2021. "An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1316-:d:570855
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    References listed on IDEAS

    as
    1. Muhammad Al-Zahrani & Amin Abo-Monasar, 2015. "Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3651-3662, August.
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    Cited by:

    1. Slim Abid & Ali M. El-Rifaie & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Ghareeb Moustafa & Mohamed A. Tolba, 2023. "Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems," Mathematics, MDPI, vol. 11(8), pages 1-32, April.
    2. Yuanfei Wei & Zalinda Othman & Kauthar Mohd Daud & Shihong Yin & Qifang Luo & Yongquan Zhou, 2022. "Equilibrium Optimizer and Slime Mould Algorithm with Variable Neighborhood Search for Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    3. Qiuyan Wang & Qingjian Zhao, 2022. "Assessing Ecological Infrastructure Investments—A Case Study of Water Rights Trading in Lu’an City, Anhui Province, China," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    4. Shahenda Sarhan & Abdullah Mohamed Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems," Mathematics, MDPI, vol. 10(12), pages 1-30, June.

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