IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v37y2023i4d10.1007_s11269-023-03447-7.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03447-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-023-03447-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.
    2. Xinxin Liu & Xiaosheng Wang & Haiying Guo & Xiaojie An, 2021. "Benefit Allocation in Shared Water-Saving Management Contract Projects Based on Modified Expected Shapley Value," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 39-62, January.
    3. Zhang, Qingsong & Sun, Jiahao & Dai, Changlei & Zhang, Guangxin & Wu, Yanfeng, 2024. "Sustainable development of groundwater resources under the large-scale conversion of dry land into rice fields," Agricultural Water Management, Elsevier, vol. 298(C).
    4. Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.
    5. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.
    6. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    7. Ieva Meidute-Kavaliauskiene & Milad Alizadeh Jabehdar & Vida Davidavičienė & Mohammad Ali Ghorbani & Saad Sh. Sammen, 2021. "A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    8. Mohsen Sharafatmandrad & Azam Khosravi Mashizi, 2021. "Temporal and Spatial Assessment of Supply and Demand of the Water-yield Ecosystem Service for Water Scarcity Management in Arid to Semi-arid Ecosystems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 63-82, January.
    9. Xiangwen Kong & Chengyan Yue & Eric Watkins & Mike Barnes & Yufeng Lai, 2023. "Investigating the Effectiveness of Irrigation Restriction Length on Water Use Behavior," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 251-268, January.
    10. Renee Obringer & Dave D. White, 2023. "Leveraging Unsupervised Learning to Develop a Typology of Residential Water Users’ Attitudes Towards Conservation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 37-53, January.
    11. 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.
    12. Jacek Wawrzosek & Syzmon Ignaciuk & Justyna Stańczyk & Joanna Kajewska-Szkudlarek, 2021. "Water Consumption Variability Based on Cumulative Data From Non-simultaneous and Long-term Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2799-2812, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-023-03447-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.