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

  • Bohan Yang
  • Weiwei Zheng
  • Xinli KeEmail author
Living reference work entry
Part of the Ecohydrology book series (ECOH)

Abstract

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.

Keywords

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

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Public AdministrationHuazhong Agricultural UniversityWuhanChina

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