【摘要】 为提升水资源配置效率,应用数据挖掘技术,对全国首次重点用水单位监控工作中所获得的约26万个用水数据进行了特征选择和用水模式区分。依据DB index准则,从用水特征中筛选出现状、愿景和波动3个特征。从这3个特征入手,采用k-means算法,将用水主体划分为5种用水模式,即均衡扩张型、均衡紧缩型、集中稳定型、波动收缩型和波动扩张型。结果表明:全国大多数用水单位现状特征集中在[0.7, 0.9]、愿景特征集中在[0.8,1.0]、波动特征集中在[0.1,0.5],较内地用水,东南沿海用水量在年内各月间波动较小。用水模式以波动收缩型为主,该模式涵盖多数产能过剩的高耗水行业;农业的用水模式为集中稳定型;高新技术与服务业的用水模式多为波动扩张型与均衡扩张型。结合不同的用水特征和用水模式,在法律、制度、监控等层面分别提出了管理建议,可为精准化、差异化节水管理提供参考。更多还原

【Abstract】 Data mining technology is employed for the feature extraction and pattern identification of about 260 thousand water use data collected in the Monitoring and Control Program of Water Use Units by the Ministry of Water Resources. Using the k-means clustering algorithm based on the Davies-Bouldin index, the dataset is categorized into three features, i.e., feature of water saving status quo as reflected by WSE, feature of expected water saving willingness as reflected by EW, and feature of monthly water saving volatility as reflected by C_v. The WSE value of most water use units concentrates in [0.7, 0.9], EW in [0.8, 1.0], and C_v in [0.1, 0.5]. Furthermore, on the basis of the features extracted above, water use pattern is classified as five groups, namely, balanced expansion pattern, balanced contraction pattern, centralized stable pattern, fluctuated contraction pattern, and fluctuated expansion pattern. Compared with inland areas, the southeast coastal areas produce less volatile monthly water consumption. Among the five water use patterns, fluctuated contraction is the dominant pattern covering most high consumption industries with excessive productivity; fluctuated expansion and balanced expansion mainly distributes in high-tech manufacturing industry and service industry; and centralized stable pattern in agricultural industry. In addition, management suggestions are put forward respecting laws and regulations and monitoring work to offer reference for a more precise and targeted water saving management.更多还原