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An Investigation of the Temporal Interaction of Urban Water Consumption in the Framework of Settlement Characteristics

Author

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  • Volkan Yilmaz

    (Konya Technical University)

  • Mehmet Alpars

    (Konya Technical University Institute of Graduate Studies)

Abstract

Can a natural or artificial phenomenon remember its past just like a human? and what are the factors affecting this memory mechanism? This study is designed to find answers to these two questions in the field of urban water consumption within the framework of the settlement characteristics. For this purpose, four different districts with different settlement characteristics belonging to the city of Konya, located in the central part of Turkey, were studied. In the study, firstly the monthly urban water consumption, population, per capita income and the different meteorological variables were used to determine the most influential parameters on water consumption with the help of Factor Analysis. Subsequently, the nonlinear water consumption models were produced with Artificial Bee Colony and Particle Swarm Optimization algorithms. In the last part of the study, the temporal interaction mechanisms were examined with the Band Similarity (BS) method, a novel approach using the model information. As a result of the study, it was observed that the phenomenon of water consumption in the studied districts remembers its own history together with the input parameters. In addition, it was concluded that there is a strong relationship structure between the population density in the settlement and the memory mechanism, and that the memory becomes stronger as the population density increases. Strong memory properties were accepted as a positive outcome, and accordingly, it was suggested by the authors that high-density residential areas are a more sustainable solution in terms of urban water management.

Suggested Citation

  • Volkan Yilmaz & Mehmet Alpars, 2023. "An Investigation of the Temporal Interaction of Urban Water Consumption in the Framework of Settlement Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1619-1639, March.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-023-03447-7
    DOI: 10.1007/s11269-023-03447-7
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    References listed on IDEAS

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    1. Jun Guo & Hui Sun & Baigang Du, 2022. "Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3385-3400, July.
    2. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.
    3. Mohammed Sanusi Shiru & Shamsuddin Shahid & Inhwan Park, 2021. "Projection of Water Availability and Sustainability in Nigeria Due to Climate Change," Sustainability, MDPI, vol. 13(11), pages 1-16, June.
    4. Hui Wang & Dave Bracciano & Tirusew Asefa, 2020. "Evaluation of Water Saving Potential for Short-Term Water Demand Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3317-3330, August.
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