A Two-stage Approach to Basin-scale Water Demand Prediction
- 111 Downloads
Water demand prediction (WDP) is the basis for water allocation. However, traditional methods in WDP, such as statistical modeling, system dynamics modeling, and the water quota method have a critical disadvantage in that they do not consider any constraints, such as available water resources and ecological water demand. This study proposes a two-stage approach to basin-scale WDP under the constraints of total water use and ecological WD, aiming to flexibly respond to a dynamic environment. The prediction method was divided into two stages: (i) stage 1, which is the prediction of the constrained total WD of the whole basin (T w ) under the constraints of available water resources and total water use quota released by the local government and (ii) stage 2, which is the allocation of T w to its subregions by applying game theory. The WD of each subregion (T s ) was predicted by calculating its weight based on selected indicators that cover regional socio-economic development and water use for different industries. The proposed approach was applied in the Dongjiang River (DjR) basin in South China. According to its constrained total water use quota and ecological WD, T w data were 7.92, 7.3, and 5.96 billion m3 at the precipitation frequencies of 50%, 90%, and 95%, respectively (in stage 1). Industrial WDs in the domestic, primary, secondary, tertiary, and environment sectors are 1.08, 2.26, 2.02, 0.44, and 0.16 billion m3, respectively, in extreme dry years (in stage 2). T w and T s exhibit structures similar to that of observed water use, mainly in the upstream and midstream regions. A larger difference is observed between T s and its total observed water use, owing to some uncertainties in calculating T w . This study provides useful insights into adaptive basin-scale water allocation under climate change and the strict policy of water resource management.
KeywordsWater demand prediction Two-stage approach Game theory Dongjiang River basin, South China
The authors would like to express their gratitude to all of the reviewers for their valuable recommendations. This study was financially supported by the National Natural Science Foundation of China (Grant No.: Grant No.: 51509127, 91547202, 51210013, 51479216, 51569009 and 91547108).
- Adamowski J, Chan HF, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in montreal, canada. Water Resour Res 48(1):273–279CrossRefGoogle Scholar
- De A Lima Neto E, de A. T. de Carvalho F, Freire ES (2005) Applying constrained linear regression models to predict interval-valued data. In Annual Conference on artificial intelligence, Springer Berlin Heidelberg, pp 92–106Google Scholar
- Guangdong Provincial Water Resources Department (2012) Control target for total water use of Guangdong province,Guangzhou, pp 8–10Google Scholar
- He YH, Lin KR, Chen XH (2013) Effect of land use and climate change on runoff in the Dongjiang Basin of South China. Math Probl Eng. https://doi.org/10.1155/2013/471429
- Lin KR, Lian YQ, Chen XH, Lu F (2014a) Changes in runoff and eco-flow in the Dongjiang River of the Pearl River basin. China Front Earth Sci. https://doi.org/10.1007/s11707-014-0434-y
- Liu CM, Wang HR (2003) An analysis of the relationship between water resources and population-economy-society-environment. Journal of Natural Resources 18(5):635–644Google Scholar
- Mohtar RH, Daher B (2016) Water-energy-food nexus framework for facilitating multi-stakeholder dialogue. Water Int 41(5):655–661Google Scholar
- Mouatadid S, Adamowski J (2016) Using extreme learning machines for short-term urban water demand forecasting. Urban Water J 14(6):630–638Google Scholar
- Pathak D, Krahenbuhl P, Darrell T (2015) Constrained convolutional neural networks for weakly supervised segmentation. IEEE Int Conf Comput Vis 1–12. Cite as:arXiv:1506.03648Google Scholar
- Yang LE, Chan FS, Scheffran J (2016) Climate change, water management and stakeholder analysis in the Dongjiang River basin in South China. Int J Water Resour Dev. https://doi.org/10.1080/07900627.2016.1264294
- Zhang Q, Cui Y, Chen YQ (2012) Ecological flow evaluation based on hydrological alterations in the Dongjiang River basin. J Nat Resour 27(5):790–800Google Scholar