数据挖掘技术在节水管理中的应用
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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。
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.
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.
引文
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[16] 肖凯,魏菲,彭昌水.基于R语言的数据挖掘在水环境管理中的应用[J].长江科学院院报,2012,29(9):91-94.
[17] 张颖,高倩倩.基于随机森林分类算法的巢湖水质评价[J].环境工程学报,2016,10(2):992-998.
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[19] 邢辉.基于模糊聚类法在康平县水资源优化配置中的应用[J].黑龙江水利科技,2018,46(7):159-162.
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[21] 赵静静.我国工业节水考核指标和评价机制研究[D].杭州:浙江理工大学,2015:1-10.
[22] 张莉,孙钢,郭军.基于无监督学习的特征选择方法[C]//2004中国控制与决策学术年会论文集.2004:218-220.
[23] ZHENG A,CASARI A.Mastering Feature Engineering Principles and Techniques for Data Scientists[M].Sebastopol,CA:O’Reill Media Publishing,2017.
[2] 李原园,曹建廷,黄火键,等.国际上水资源综合管理进展[J].水科学进展,2018,29(1):127-137.
[3] 王喜峰,张景增.水资源管理的供给侧结构性改革研究[J].水利经济,2018,36(1):42-45.
[4] 严登华,王浩,张建云,等.生态海绵智慧流域建设:从状态改变到能力提升[J].水科学进展,2017,28(2):302-310.
[5] 安正本.我国水资源管理模式分类及效果比较研究[J].科技创新与应用,2018(34):195-196.
[6] 陈茂山,陈博.关于运用精准思维推进精准节水的思考[J].中国水利,2018(23):1-3.
[7] 水利部综合事业局.关于加强重点监控用水单位监督管理工作的通知[R].北京:水利部综合事业局,2016.
[8] 姜华.基于数据挖掘的水务信息管理系统与决策平台[D].北京:北京邮电大学,2018:10-16.
[9] 张小娟.数据挖掘技术在水资源领域的应用研究[D].北京:中国水利水电科学研究院,2007:10-15.
[10] KANTARDZ M,ZURADA J,SHORE K.Data Mining Concepts,Models,Methods,and Algorithms[J].IEEE Transactions,2005,36(5):495-496.
[11] 韩红旗.数据挖掘技术在水利工程管理中的应用研究[J].中国管理信息化,2010 (4) :76-79.
[12] 王光宏,蒋平.数据挖掘综述[J].同济大学学报(自然科学版),2004,32(2):246-252.
[13] KHARROUBI O,MASSON E,BLANPAIN O,et al.ANN Modeling for Flood Prediction in the Upstream Eure’s Catchment (France)[C]//EGU General Assembly 2013,Vienna,Austria,April 7-12,2013:id8623.
[14] 周彦辰,胡铁松,陈进,等.耦合动态方程的神经网络模型在水质预测中的应用[J].长江科学院院报,2017,34(9):1-5.
[15] 李宏伟.基于关联规则的数据挖掘技术在中长期水文预报中的应用[J].人民珠江,2013,34(6):21-25.
[16] 肖凯,魏菲,彭昌水.基于R语言的数据挖掘在水环境管理中的应用[J].长江科学院院报,2012,29(9):91-94.
[17] 张颖,高倩倩.基于随机森林分类算法的巢湖水质评价[J].环境工程学报,2016,10(2):992-998.
[18] 吴琼,常浩娟,刘昭.基于聚类的我国各地区水资源利用效率分析[J].人民长江,2018,49(14):55-60.
[19] 邢辉.基于模糊聚类法在康平县水资源优化配置中的应用[J].黑龙江水利科技,2018,46(7):159-162.
[20] GB/T 4754—2017,国民经济行业分类[S].北京:中国质检出版社,2017.
[21] 赵静静.我国工业节水考核指标和评价机制研究[D].杭州:浙江理工大学,2015:1-10.
[22] 张莉,孙钢,郭军.基于无监督学习的特征选择方法[C]//2004中国控制与决策学术年会论文集.2004:218-220.
[23] ZHENG A,CASARI A.Mastering Feature Engineering Principles and Techniques for Data Scientists[M].Sebastopol,CA:O’Reill Media Publishing,2017.
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