IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v25y2011i6p1653-1676.html
   My bibliography  Save this article

Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

Author

Listed:
  • Mukand Babel
  • Victor Shinde

Abstract

The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply systems. Further, the need for such identification becomes more pronounced when data constraints arise. The objective of this study was to establish, using correlation and sensitivity analyses, a minimum set of variables required to predict water demand with significant accuracy. Artificial Neural Networks (ANN) models were developed to predict short-term (daily) and medium-term (monthly) demands for Bangkok. Using meteorological and water utility variables for short-term prediction, and different ANN architecture, 16 sets of models with a 1-, 2- and 3-day lead period were developed. Although the best fit models for the three lead periods used different input variables, prediction accuracies over 98% were achieved by using only the historic daily demand (HDD) as the explanatory variable. Similarly, for medium-term prediction, 11 sets of models with lead periods of 1-, 2- and 6-months were developed, using meteorological, water utility and socioeconomic variables. The best fit models for the three lead periods used all explanatory variables but prediction accuracies of more than 98% were obtained by downsizing the variable set. The meteorological variables have a greater influence on medium-term prediction as compared to short-term prediction, suggesting that future water demand in Bangkok could be significantly affected by climate change. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Mukand Babel & Victor Shinde, 2011. "Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1653-1676, April.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:6:p:1653-1676
    DOI: 10.1007/s11269-010-9766-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-010-9766-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-010-9766-x?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. M. Babel & A. Gupta & P. Pradhan, 2007. "A multivariate econometric approach for domestic water demand modeling: An application to Kathmandu, Nepal," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 573-589, March.
    2. Mahmut Firat & Mehmet Yurdusev & Mustafa Turan, 2009. "Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 617-632, March.
    3. Ashu Jain & Ashish Kumar Varshney & Umesh Chandra Joshi, 2001. "Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(5), pages 299-321, October.
    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. Md Haque & Ataur Rahman & Dharma Hagare & Golam Kibria, 2014. "Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1959-1971, May.
    2. 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.
    3. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.
    4. Mukand Babel & Nisuchcha Maporn & Victor Shinde, 2014. "Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2049-2062, May.
    5. Salah L. Zubaidi & Sadik K. Gharghan & Jayne Dooley & Rafid M. Alkhaddar & Mawada Abdellatif, 2018. "Short-Term Urban Water Demand Prediction Considering Weather Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4527-4542, November.
    6. Sara Fontdecaba & Pere Grima & Lluís Marco & Lourdes Rodero & José Sánchez-Espigares & Ignasi Solé & Xavier Tort-Martorell & Dominique Demessence & Victor Martínez De Pablo & Jordi Zubelzu, 2012. "A Methodology to Model Water Demand based on the Identification of Homogenous Client Segments. Application to the City of Barcelona," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 499-516, January.
    7. Carvalho, Isabella de Castro & Calijuri, Maria Lúcia & Assemany, Paula Peixoto & Silva, Marcos Dornelas Freitas Machado e & Moreira Neto, Ronan Fernandes & Santiago, Aníbal da Fonseca & de Souza, Maur, 2013. "Sustainable airport environments: A review of water conservation practices in airports," Resources, Conservation & Recycling, Elsevier, vol. 74(C), pages 27-36.
    8. C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    9. Caspar V. C. Geelen & Doekle R. Yntema & Jaap Molenaar & Karel J. Keesman, 2021. "Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1183-1196, March.

    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. Sholpan Saimova & Gulsim Makenova & Aizhan Skakova & Aitolkyn Moldagaliyeva & Ardak Beisembinova & Zhamilya Berdiyarova & Bagdagul Imanbekova, 2020. "Towards a Low-carbon Economic Sustainable Development: Scenarios and Policies for Kazakhstan," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 638-646.
    2. Mahmut Firat & Mehmet Yurdusev & Mustafa Turan, 2009. "Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 617-632, March.
    3. Salah L. Zubaidi & Sadik K. Gharghan & Jayne Dooley & Rafid M. Alkhaddar & Mawada Abdellatif, 2018. "Short-Term Urban Water Demand Prediction Considering Weather Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4527-4542, November.
    4. Xiao-Chen Yuan & Yi-Ming Wei & Su-Yan Pan & Ju-Liang Jin, 2014. "Urban Household Water Demand in Beijing by 2020: An Agent-Based Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2967-2980, August.
    5. 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.
    6. Md Haque & Ataur Rahman & Dharma Hagare & Golam Kibria, 2014. "Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1959-1971, May.
    7. Mohamed Mohamed & Aysha Al-Mualla, 2010. "Water Demand Forecasting in Umm Al-Quwain (UAE) Using the IWR-MAIN Specify Forecasting Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4093-4120, November.
    8. Xiao-jun Wang & Jian-yun Zhang & Shamsuddin Shahid & En-hong Guan & Yong-xiang Wu & Juan Gao & Rui-min He, 2016. "Adaptation to climate change impacts on water demand," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 21(1), pages 81-99, January.
    9. 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.
    10. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    11. Young Hwan Choi & Donghwi Jung, 2020. "Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    12. N. Graveline & B. Aunay & J. Fusillier & J. Rinaudo, 2014. "Coping with Urban & Agriculture Water Demand Uncertainty in Water Management Plan Design: the Interest of Participatory Scenario Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3075-3093, August.
    13. Mukand Babel & Nisuchcha Maporn & Victor Shinde, 2014. "Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2049-2062, May.
    14. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    15. Vinit Sehgal & Rajeev Sahay & Chandranath Chatterjee, 2014. "Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1733-1749, April.
    16. Henrique Monteiro, 2010. "Residential Water Demand in Portugal: checking for efficiency-based justifications for increasing block tariffs," Working Papers Series 1 ercwp0110, ISCTE-IUL, Business Research Unit (BRU-IUL).
    17. Zheng Zeng & Wei-Ge Luo & Fa-Cheng Yi & Feng-Yu Huang & Cheng-Xia Wang & Yi-Ping Zhang & Qiang-Qiang Cheng & Zhe Wang, 2021. "Horizontal Distribution of Cadmium in Urban Constructed Wetlands: A Case Study," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    18. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    19. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    20. Negin Ashoori & David A. Dzombak & Mitchell J. Small, 2016. "Modeling the Effects of Conservation, Demographics, Price, and Climate on Urban Water Demand in Los Angeles, California," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5247-5262, November.

    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:25:y:2011:i:6:p:1653-1676. 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.