Data Mining Model in Regional Target Decomposition of Water Resources Utilization Efficiency

• Published in: Applied Mechanics and Materials Vol. 580-583; pp. 2082 - 2087
• Main Authors: Fu, Shi Dong, Zhang, Xin, Huang, Ji Wen, Chen, Hua Wei, Li, Bing
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.07.2014
• Subjects: Cities; Cluster analysis; Clustering; Correlation analysis; Data mining; Decomposition; Discriminant analysis; Efficiency; Regression analysis; Resource utilization; Statistical analysis; Time series; Variance analysis; Water resources; Water Utilization Efficiency; Shandong Province; ARIMA Model; K-Means Clustering; Target Decomposition
• ISBN: 9783038351658; 3038351652
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.580-583.2082

Data mining model is the most important technical basis of the control target decomposition for the most stringent water resources management of Shandong province. K-means clustering model is adopted to analysis the water withdrawal of industrial added value per ten thousand yuan in 2010. Based on the yearly industrial water consumption trend from 1995 to 2010 of 17 municipal-level cities in Shandong province, the ARIMA (p, d, q) model is established through a lot of fitting and optimization and then the regional industrial water demand and water utilization efficiency in 2015 were forecasted. According to the proposed principal and technical route of target decomposition, the industrial water utilization efficiency target in 2015 of the whole province and 17 municipal-level cities are defined respectively.