Forecasting Industrial Water Demand Using Case Based Reasoning: A Case Study in Zhangye City, China

Living reference work entry
Part of the Ecohydrology book series (ECOH)


Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye, China. Case-based reasoning uses past experience to solve new problems. Since CBR is a methodology rather than a technique, this definition makes case-based reasoning system be an open system, which can constantly absorb new technologies and methods, and be more conducive to the development of itself. This research constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases. The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the target case, will reach 11.9 million tons. There are ten original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. This research also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and back propagation neural network in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning.


Industrial water demand Forecast Case-based reasoning Water resources management Zhangye city BP neural network Artificial intelligence Grey incidence analysis Case similarity Grey model 


  1. J.F. Adamowski, Peak daily water demand forecast modeling using artificial neural networks. J. Water Resour. Plan. Manag. 134, 119–128 (2008)CrossRefGoogle Scholar
  2. J. Adamowski, C. Karapataki, Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms. J. Hydrol. Eng. 15, 729–743 (2010)CrossRefGoogle Scholar
  3. K. Amailef, J. Lu, Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decis. Support. Syst. 55, 79–97 (2013)CrossRefGoogle Scholar
  4. Y. Bai, P. Wang, C. Li, J. Xie, Y.A. Wang, Multi-scale relevance vector regression approach for daily urban water demand forecasting. J. Hydrol. 517, 236–245 (2014)CrossRefGoogle Scholar
  5. M. Bai, S. Zhou, M. Zhao, J. Yu, Water use efficiency improvement against a backdrop of expanding city agglomeration in developing countries—A case study on industrial and agricultural water use in the Bohai Bay region of China. Water 9, 89 (2017)CrossRefGoogle Scholar
  6. J.J. Bello-Tomás, P.A. González-Calero, B. Díaz-Agudo, Jcolibri: An object-oriented framework for building CBR systems, in Advances in Case-Based Reasoning, Proceedings of the European Conference on Case-Based Reasoning 2004, (Springer, Berlin, Heidelberg, 2004), pp. 32–46Google Scholar
  7. R. Bergmann, A. Stahl, Similarity measures for object-oriented case representations, in Proceedings of the Forth European Workshop on Case-Based Reasoning, (Springer, Verlag, 1998), pp. 25–36Google Scholar
  8. G. Chen, J. Yu, Two sub-swarms particle swarm optimization algorithm, in Advances in Natural Computation, Procedings of the International Conference on Natural Computation, (Springer, Berlin, Heidelberg, 2005), pp. 515–524Google Scholar
  9. R. Connor, The United Nations world water development report 2015: Water for a sustainable world (UNESCO Publishing, Paris, 2015)Google Scholar
  10. R.L. De Mantaras, D. McSherry, D. Bridge, D. Leake, B. Smyth, S. Craw, M. Keane, Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20, 215–240 (2005)CrossRefGoogle Scholar
  11. X. Deng, C. Zhao, Identification of water scarcity and providing solutions for adapting to climate changes in the Heihe River Basin of China. Adv. Meteorol (2015).
  12. X. Deng, F. Zhang, Z. Wang, X. Li, T. Zhang, An extended input output table compiled for analyzing water demand and consumption at county level in China. Sustainability 6, 3301–3320 (2014)CrossRefGoogle Scholar
  13. S. Ding, C. Su, J. Yu, An optimizing BP neural network algorithm based on genetic algorithm. Artif. Intell. Rev. 36, 153–162 (2011)CrossRefGoogle Scholar
  14. Y. Du, W. Wen, F. Cao, M. Ji, A case-based reasoning approach for land use change prediction. Expert Syst. Appl. 37, 5745–5750 (2010)CrossRefGoogle Scholar
  15. C.Y. Fan, P.C. Chang, J.J. Lin, J.C. Hsieh, A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11, 632–644 (2011)CrossRefGoogle Scholar
  16. S. Gato, N. Jayasuriya, P. Roberts, Temperature and rainfall thresholds for base use urban water demand modelling. J. Hydrol. 337, 364–376 (2007)CrossRefGoogle Scholar
  17. Q. Guan, L. Wang, K.C. Clarke, An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartogr. Geogr. Info. Sci. 32, 369–380 (2005)CrossRefGoogle Scholar
  18. M.M. Haque, A. Rahman, D. Hagare, G. Kibria, Principal component regression analysis in water demand forecasting: An application to the Blue Mountains, NSW, Australia. J. Hydro. Environ. Res. 1, 49–59 (2016)Google Scholar
  19. C. Harpham, C.W. Dawson, M.R. Brown, A review of genetic algorithms applied to training radial basis function networks. Neural Comput. Appl. 13, 193–201 (2004)CrossRefGoogle Scholar
  20. A. Holt, I. Bichindaritz, R. Schmidt, P. Perner, Medical applications in case-based reasoning. Knowl. Eng. Rev. 20, 289–292 (2005)CrossRefGoogle Scholar
  21. M.J. Huang, M.Y. Chen, S.C. Lee, Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Syst. Appl. 32, 856–867 (2007)CrossRefGoogle Scholar
  22. I.M. Johannsen, J.C. Hengst, A. Goll, B. Höllermann, B. Diekkrüger, Future of water supply and demand in the Middle Drâa Valley, Morocco, under climate and land use change. Water 8, 313 (2016)CrossRefGoogle Scholar
  23. D.H. Jonassen, J. Hernandez-Serrano, Case-based reasoning and instructional design: Using stories to support problem solving. Educ. Technol. Res. Dev. 50, 65–77 (2002)CrossRefGoogle Scholar
  24. D. Katz, Water use and economic growth: Reconsidering the Environmental Kuznets Curve relationship. J. Clean. Prod. 88, 205–213 (2015)CrossRefGoogle Scholar
  25. J. Kolodner, Case-Based Reasoning (Morgan Kaufmann, San Meteo, 1993)CrossRefGoogle Scholar
  26. D.B. Leake, Problem solving and reasoning: Case-based. Int. Encycl. Soc. Behav. Sci. 2015, 56–60 (2015)CrossRefGoogle Scholar
  27. X. Li, X. Liu, An extended cellular automaton using case-based reasoning for simulating urban development in a large complex region. Int. J. Geogr. Inf. Sci. 20, 1109–1136 (2006)CrossRefGoogle Scholar
  28. Y.F. Li, M. Xie, T.N.A. Goh, study of mutual information based feature selection for case-based reasoning in software cost estimation. Expert Syst. Appl. 36, 5921–5931 (2009)CrossRefGoogle Scholar
  29. J. Liu, H.H. Savenije, J. Xu, Forecast of water demand in Weinan city in China using WDF-ANN model. Phys. Chem. Earth Parts A/B/B 28, 219–224 (2003)CrossRefGoogle Scholar
  30. W. Liu, G. Hu, J. Li, Emergency resources demand prediction using case-based reasoning. Saf. Sci. 50, 530–534 (2012)CrossRefGoogle Scholar
  31. T. Madhusudan, J.L. Zhao, B. Marshall, A case-based reasoning framework for workflow model management. Data Knowl. Eng. 50, 87–115 (2004)CrossRefGoogle Scholar
  32. M.M. Mekonnen, A.Y. Hoekstra, Four billion people facing severe water scarcity. Sci. Adv. 2, e1500323 (2016)CrossRefGoogle Scholar
  33. X. Mo, S. Liu, Z. Lin, Y. Xu, Y. Xiang, T.R. Mcvicar, Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecol. Model. 183, 301–322 (2005)CrossRefGoogle Scholar
  34. M.M. Mohamed, A.A. Almualla, Water demand forecasting in Umm Al-Quwain (UAE) using the the IWR-MAIN specify forecasting mode. Water Resour. Manag. 24, 4093–4120 (2010)CrossRefGoogle Scholar
  35. Y. Nian, X. Li, J. Zhou, X. Hu, Impact of land use change on water resource allocation in the middle reaches of the Heihe River Basin in northwestern China. J. Arid. Land 6, 273–286 (2014)CrossRefGoogle Scholar
  36. E. Olsson, P. Funk, N. Xiong, Fault diagnosis in industry using sensor readings and case-based reasoning. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 15, 41–46 (2004)Google Scholar
  37. I. Pulido-Calvo, P. Montesinos, J. Roldán, F. Ruiznavarro, Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems. Biosyst. Eng. 97, 283–293 (2007)CrossRefGoogle Scholar
  38. E.R. Reyes, S. Negny, G.C. Robles, J.M. Le Lann, Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design. Eng. Appl. Artif. Intell. 41, 1–16 (2015)CrossRefGoogle Scholar
  39. F.R. Rijsberman, Water scarcity: Fact or fiction? Agric. Water Manag. 80, 5–22 (2006)CrossRefGoogle Scholar
  40. E. Salajegheh, S. Gholizadeh, Optimum design of structures by an improved genetic algorithm using neural networks. Adv. Eng. Softw. 36, 757–767 (2005)CrossRefGoogle Scholar
  41. Y. Shen, J. Colloc, A. Jacquet-Andrieu, L. Kai, Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. J. Biomed. Inf. 56, 307–317 (2015)CrossRefGoogle Scholar
  42. K.S. Shin, I. Han, Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Syst. Appl. 16, 85–95 (1999)CrossRefGoogle Scholar
  43. K.S. Shin, I. Han, A case-based approach using inductive indexing for corporate bond rating. Decis. Support. Syst. 32, 41–52 (2001)CrossRefGoogle Scholar
  44. R. Venkatesan, V. Kumar, A genetic algorithms approach to growth phase forecasting of wireless subscribers. Int. J. Forecast. 18, 625–646 (2002)CrossRefGoogle Scholar
  45. E.K. Weatherhead, J.W. Knox, Predicting and mapping the future demand for irrigation water in England and Wales. Agric. Water Manag. 43, 203–218 (2000)CrossRefGoogle Scholar
  46. B.S. Yang, T. Han, Y.S. Kim, Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis. Expert Syst. Appl. 26, 387–395 (2004)CrossRefGoogle Scholar
  47. Y. Zhai, J. Wang, Y. Teng, R. Zuo, Water demand forecasting of beijing using the time series forecasting method. J. Geogr. Sci. 22, 919–932 (2012)CrossRefGoogle Scholar
  48. Q. Zhang, Y. Diao, J. Dong, Regional water demand prediction and analysis based on Cobb-Douglas model. Water Resour. Manag. 27, 3103–3113 (2013)CrossRefGoogle Scholar
  49. K. Zhao, X. Yu, A case-based reasoning approach on supplier selection in petroleum enterprises. Expert Syst. Appl. 38, 6839–6847 (2011)CrossRefGoogle Scholar

Authors and Affiliations

  1. 1.College of Public AdministrationHuazhong Agricultural UniversityWuhanChina

Personalised recommendations