IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i12d10.1007_s11269-021-02927-y.html
   My bibliography  Save this article

Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Author

Listed:
  • Xin Liu

    (North China University of Water Resources and Electric Power
    Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Xuefeng Sang

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Jiaxuan Chang

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Yang Zheng

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

Abstract

The water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99).

Suggested Citation

  • Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02927-y
    DOI: 10.1007/s11269-021-02927-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02927-y
    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/s11269-021-02927-y?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. Jisi Fu & Ping-an Zhong & Juan Chen & Bin Xu & Feilin Zhu & Yu Zhang, 2019. "Water Resources Allocation in Transboundary River Basins Based on a Game Model Considering Inflow Forecasting Errors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2809-2825, June.
    2. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    3. Arpit Tripathi & Simran Kaur & Suresh Sankaranarayanan & Lakshmi Kanthan Narayanan & Rijo Jackson Tom, 2019. "Water Demand Prediction for Housing Apartments Using Time Series Analysis," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 15(4), pages 57-75, October.
    4. Yuebing Xu & Jing Zhang & Zuqiang Long & Yan Chen, 2018. "A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting," Energies, MDPI, vol. 11(5), pages 1-15, April.
    5. Chen, Li-Juan & Feng, Qi & Li, Feng-Rui & Li, Chang-Sheng, 2014. "A bidirectional model for simulating soil water flow and salt transport under mulched drip irrigation with saline water," Agricultural Water Management, Elsevier, vol. 146(C), pages 24-33.
    6. A. B. Dariane & M. Farhani & Sh Azimi, 2018. "Long Term Streamflow Forecasting Using a Hybrid Entropy Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1439-1451, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.

    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. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    2. Li, Yunfeng & Yu, Qihua & Ning, Huifeng & Gao, Yang & Sun, Jingsheng, 2023. "Simulation of soil water, heat, and salt adsorptive transport under film mulched drip irrigation in an arid saline-alkali area using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 290(C).
    3. Zhang, You-Liang & Feng, Shao-Yuan & Wang, Feng-Xin & Binley, Andrew, 2018. "Simulation of soil water flow and heat transport in drip irrigated potato field with raised beds and full plastic-film mulch in a semiarid area," Agricultural Water Management, Elsevier, vol. 209(C), pages 178-187.
    4. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    5. Banaś, Jan & Utnik-Banaś, Katarzyna, 2021. "Evaluating a seasonal autoregressive moving average model with an exogenous variable for short-term timber price forecasting," Forest Policy and Economics, Elsevier, vol. 131(C).
    6. Wang, Zhanwei & Song, Woon-Kyung, 2020. "Sustainable airport development with performance evaluation forecasts: A case study of 12 Asian airports," Journal of Air Transport Management, Elsevier, vol. 89(C).
    7. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    8. Chen, Ning & Li, Xianyue & Šimůnek, Jirí & Shi, Haibin & Ding, Zongjiang & Peng, Zunyuan, 2019. "Evaluating the effects of biodegradable film mulching on soil water dynamics in a drip-irrigated field," Agricultural Water Management, Elsevier, vol. 226(C).
    9. Nguyen, Quang Hai, 2024. "Modeling the volatility of international air freight: A case study of Singapore using the SARIMAX-EGARCH model," Journal of Air Transport Management, Elsevier, vol. 117(C).
    10. Suh, Daniel Y. & Ryerson, Megan S., 2019. "Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 400-416.
    11. Cundong Xu & Junjiao Tian & Guoxia Wang & Junkun Nie & Hongyang Zhang, 2019. "Dynamic Simulation of Soil Salt Transport in Arid Irrigation Areas under the HYDRUS-2D-Based Rotation Irrigation Mode," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3499-3512, August.
    12. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    13. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    14. Saglam, Mustafa & Sintim, Henry Y. & Bary, Andy I. & Miles, Carol A. & Ghimire, Shuresh & Inglis, Debra A. & Flury, Markus, 2017. "Modeling the effect of biodegradable paper and plastic mulch on soil moisture dynamics," Agricultural Water Management, Elsevier, vol. 193(C), pages 240-250.
    15. Wang, Jianxin & Lim, Ming K. & Zhan, Yuanzhu & Wang, XiaoFeng, 2020. "An intelligent logistics service system for enhancing dispatching operations in an IoT environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    16. Wang, Xiangping & Liu, Guangming & Yang, Jingsong & Huang, Guanhua & Yao, Rongjiang, 2017. "Evaluating the effects of irrigation water salinity on water movement, crop yield and water use efficiency by means of a coupled hydrologic/crop growth model," Agricultural Water Management, Elsevier, vol. 185(C), pages 13-26.
    17. Tirtha, Sudipta Dey & Bhowmik, Tanmoy & Eluru, Naveen, 2024. "Accommodating spatio-temporal dependency in airline demand modeling," Journal of Air Transport Management, Elsevier, vol. 116(C).
    18. Fanyu Meng & Wenwu Gong & Jun Liang & Xian Li & Yiping Zeng & Lili Yang, 2021. "Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
    19. Dorta-Santos, María & Tejedor, Marisa & Jiménez, Concepción & Hernández-Moreno, Jose M. & Díaz, Francisco J., 2016. "“Using marginal quality water for an energy crop in arid regions: Effect of salinity and boron distribution patterns”," Agricultural Water Management, Elsevier, vol. 171(C), pages 142-152.
    20. Agnieszka Barczak & Izabela Dembińska & Dorota Rozmus & Katarzyna Szopik-Depczyńska, 2022. "The Impact of COVID-19 Pandemic on Air Transport Passenger Markets-Implications for Selected EU Airports Based on Time Series Models Analysis," Sustainability, MDPI, vol. 14(7), pages 1-19, April.

    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:35:y:2021:i:12:d:10.1007_s11269-021-02927-y. 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.