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Water 2017, 9(8), 626; https://doi.org/10.3390/w9080626

Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China

College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
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Received: 30 June 2017 / Revised: 3 August 2017 / Accepted: 18 August 2017 / Published: 22 August 2017
(This article belongs to the Special Issue Sustainable Water Management within Inland River Watershed)
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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. We 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 t arget case, will reach 11.9 million tons. There are 10 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. We also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and BPN in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning. View Full-Text
Keywords: industrial water demand; forecast; case-based reasoning; water resources management; Zhangye city industrial water demand; forecast; case-based reasoning; water resources management; Zhangye city
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Yang, B.; Zheng, W.; Ke, X. Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China. Water 2017, 9, 626.

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