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Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok

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  • Mukand Babel
  • Nisuchcha Maporn
  • Victor Shinde

Abstract

With concerns relating to climate change, and its impacts on water supply, there is an increasing emphasis on water utilities to prepare for the anticipated changes so as to ensure sustainability in supply. Forecasting the water demand, which is done through a variety of techniques using diverse explanatory variables, is the primary requirement for any planning and management measure. However, hitherto, the use of future climatic variables in forecasting the water demand has largely been unexplored. To plug this knowledge gap, this study endeavored to forecast the water demand for the Metropolitan Waterworks Authority (MWA) in Thailand using future climatic and socioeconomic data. Accordingly, downscaled climate data from HadCM3 and extrapolated data of socioeconomic variables was used in the model development, using Artificial Neural Networks (ANN). The water demand was forecasted at two scales: annual and monthly, up to the year 2030, with good prediction accuracy (AAREs: 4.76 and 4.82 % respectively). Sensitivity analysis of the explanatory variables revealed that climatic variables have very little effect on the annual water demand. However, the monthly demand is significantly affected by climatic variables, and subsequently climate change, confirming the notion that climate change is a major constraint in ensuring water security for the future. Because the monthly water demand is used in designing storage components of the supply system, and planning inter-basin transfers if required, the results of this study provide the MWA with a useful reference for designing the water supply plan for the years ahead. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:7:p:2049-2062
    DOI: 10.1007/s11269-014-0598-y
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    References listed on IDEAS

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    1. Salvatore Campisi-Pinto & Jan Adamowski & Gideon Oron, 2012. "Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3539-3558, September.
    2. 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.
    3. 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.
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    1. 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.
    2. Zhihao Xu & Zhiqiang Lv & Jianbo Li & Anshuo Shi, 2022. "A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4293-4312, September.
    3. Diana Fiorillo & Zoran Kapelan & Maria Xenochristou & Francesco De Paola & Maurizio Giugni, 2021. "Assessing the Impact of Climate Change on Future Water Demand using Weather Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1449-1462, March.
    4. Yanhu He & Jie Yang & Xiaohong Chen & Kairong Lin & Yanhui Zheng & Zhaoli Wang, 2018. "A Two-stage Approach to Basin-scale Water Demand Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 401-416, January.
    5. Xiao-Jun Wang & Jian-Yun Zhang & Shamsuddin Shahid & Wei Xie & Chao-Yang Du & Xiao-Chuan Shang & Xu Zhang, 2018. "Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 911-924, April.

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