基于耦合模型的流域污染负荷分配实例
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摘要
流域污染负荷分配是实施流域精细化管理的重要措施。在指标选取过程中,为了定量描述评价指标对流域污染负荷分配的重要性,引入随机森林算法对各指标作重要性评价,依据重要性程度筛选涵盖全部影响因素的评价指标,并进一步确定各指标权重。将评价指标体系与环境基尼系数模型相结合,构建污染负荷分配耦合模型,并以清潩河流域(许昌段)为例,制定基于控制单元的COD负荷分配方案。结果表明,满足重要性评分要求的指标分别是居民人均收入、单位GDP污水产生量、工业企业水消费量、地表水资源量、控制单元面积,评分分别为13.97、9.65、8.77、5.21、1.09。依据耦合模型制定的2020年清潩河流域(许昌段)各控制单元COD负荷分配方案中,控制单元QYH-3的削减量和削减率均最大,分别为617.14t/a和30.44%。分配方案基本符合流域实际,结果较为科学合理。
Watershed pollution load distribution is an important measure to implement watershed fine management.In order to quantitatively describe the significance of assessment indicators on the distribution of pollution load in river basin,the random forest algorithm was adopted to evaluate the importance of the selected indicators.The evaluation indices,which can reflect all the influence factors,were selected based on their importance,then,the weight coefficient of each indicator was further determined based on its score.The coupling model of pollution load distribution is constructed by combining the evaluation index system with the environmental gini coefficient model.Additionally,allocation plan of COD load was formulated based on control unit in the target Qingyi river basin(Xuchang Section).We found that the indicators,which fulfill the requirements of importance score,include per capita income,sewage production per unit of GDP,water consumption of industrial and enterprises,the amount of surface water resources and area of control-unit,and the scores are 13.97,9.65,8.77,5.21 and 1.09,respectively.The allocation plan of COD load in the river basin on 2020 was further formulated based on the coupling model,and the pollutant reduction and reduction rate of the control-unit QYH-3 are the largest,which are 617.14 t/a and 30.44 %,respectively.The allocation plan is reasonable for reflecting the actual situation of the basin.
Watershed pollution load distribution is an important measure to implement watershed fine management.In order to quantitatively describe the significance of assessment indicators on the distribution of pollution load in river basin,the random forest algorithm was adopted to evaluate the importance of the selected indicators.The evaluation indices,which can reflect all the influence factors,were selected based on their importance,then,the weight coefficient of each indicator was further determined based on its score.The coupling model of pollution load distribution is constructed by combining the evaluation index system with the environmental gini coefficient model.Additionally,allocation plan of COD load was formulated based on control unit in the target Qingyi river basin(Xuchang Section).We found that the indicators,which fulfill the requirements of importance score,include per capita income,sewage production per unit of GDP,water consumption of industrial and enterprises,the amount of surface water resources and area of control-unit,and the scores are 13.97,9.65,8.77,5.21 and 1.09,respectively.The allocation plan of COD load in the river basin on 2020 was further formulated based on the coupling model,and the pollutant reduction and reduction rate of the control-unit QYH-3 are the largest,which are 617.14 t/a and 30.44 %,respectively.The allocation plan is reasonable for reflecting the actual situation of the basin.
引文
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[17]杨芳．基于基尼系数法的九龙江流域水污染物排放总量分配研究[J]．环境科学与管理，2012,37(5):30-35.(YANG F.Research on total emission allocation of water pollutants in jiulong river basin based on gini coefficient[J].Environmental Science and Management,2012,37(5):30-35.(in Chinese))
[18]李泽琪，张玥，王晓燕，等．基于不同层级排污单元的水污染负荷分配方法[J]．资源科学，2018,40(7):1429-1437.(LI Z Q,ZHANG Y,WANG X Y,et al.Water pollution load distribution method based on different levels of pollutant discharge units[J].Resources science,2018,40(7):1429-1437.(in Chinese))DOI:10.18402/resci.2018.07.11.
[19]赵雪霞，于鲁冀，王燕鹏．清潩河流域（许昌段）水生态环境功能分区指标体系构建[J]．水利水电技术，2018,49(9):165-172.(YU L J ZHAO X X,WANG Y P.Qingyi river basin(Xuchang)water environment functional zoning index system to build[J].Water Resources and Hydropower Engineering,2018,49(9):165-172.(in Chinese))DOI:10.13928/j.cnki.wrahe.2018.09.022.
[20]田平，方晓波，王飞儿，等．基于环境基尼系数最小化模型的水污染物总量分配优化———以张家港平原水网区为例[J]．中国环境科学，2014,34(3):801-809.(TIAN P,FANG X B,WANG F E,et al.Optimization of total water pollutant distribution based on environmental gini coefficient minimization model—a case study of water network area in zhangjiagang plain[J].China Environmental Science,2014,34(3):801-809.(in Chinese))
[21] BOSI S,SEEGMULLER T.Optimal cycles and social inequality:what do we learn from the gini index[J].Research in Economics,2006,60(1):35-46.DOI:10.1016/j.rie.2005.12.003.
[22]王媛，牛志广，王伟．基尼系数法在水污染物总量区域分配中的应用[J]．中国人口·资源与环境，2008,18(3):177-180.(WANG Y,NIU Z G,WANG W.Application of gini coefficient method in regional distribution of total water pollutants[J].China Population Resources and Environment,2008,18(3):177-180).
[23]赵骞．河流和海洋污染物总量分配研究述评[J]．中国人口·资源与环境，2014,24(3):82-86.(ZHAO Q.Review of studies on total pollutant distribution in rivers and oceans[J].China Population Resources and Environment,2014,24(3):82-86.(in Chinese))DOI:10.12062/cpre.20170307.
[24]何帮强，洪兴建．基尼系数计算与分解方法研究综述[J]．统计与决策，2016(14):13-17.(HE B Q,HONG X J.Research review on gini coefficient calculation and decomposition methods[J].Statistics&Decision,2016(14):13-17.(in Chinese))DOI:10.13546/j.cnki.tjyjc.2016.14.003.
[25] CUTLER A,CUTLER D R,STEVENS J R.Random forests[J].Machine Learning,2004,45(1):157-176.DOI:10.1007/978-1-4419-9326-7_5.
[26] BIFET A,HOLMES G,PFAHRINGER B.Leveraging bagging for evolving data streams[C].European Conference on Machine Learning&Knowledge Discovery in Databases,2010.DOI:10.1007/978-3-642-15880-3_15.
[27]冯盼峰，温永仙．基于随机森林算法的两阶段变量选择研究[J]．系统科学与数学，2018,38(1):119-130.(FENG P F,WEN Y X.Two-stage variable selection based on random forest algorithm[J].Journal of Systems Science and Mathematical Sciences,2018,38(1):119-130.(in Chinese))
[28]方匡南，吴见彬，朱建平，等．随机森林方法研究综述[J]．统计与信息论坛，2011,26(3):32-38.(FANG K N,WU J B,ZHU J P,et al.Research review of random forest method[J].Statistics&Information Forum,2011,26(3):32-38.(in Chinese))
[29] RAMN DAZ-URIARTE,SARA ALVAREZ DE ANDRS.Gene selection and classification of microarray data using random forest[J].Bmc Bioinformatics,2006,7(1):3-0.DOI:10.1186/1471-2105-7-3.
[30] GREGORUTTI B,MICHEL B,SAINT-PIERRE P.Correlation and variable importance in random forests[J].Statistics and Computing,2017,27(3):659-678.DOI:10.1007/s11222-016-9646-1.
[2]赵永宏，邓祥征，吴锋，等．乌梁素海流域氮磷减排与区域经济发展的均衡分析[J]．环境科学研究，2011,24(1):110-117.(ZHAO Y H,DENG X Z,WU F,et al.Equilibrium analysis of nitrogen and phosphorus emission reduction and regional economic development in wuliangsuhai basin[J].Research of Environmental Sciences,2011,24(1):110-117.(in Chinese))DOI:10.13198/j.res.2011.01.112.zhaoyh.017.
[3]秦迪岚，黄哲，罗岳平，等．洞庭湖区污染控制区划与控制对策[J]．环境科学研究，2011,24(7):748-755.(QIN D L,HUANG Z,LUO Y P,et al.Pollution control regionalization and control countermeasures in dongting lake area[J].Research of Environmental Sciences,2011,24(7):748-755.(in Chinese))DOI:10.13198/j.res.2011.07.40.qindl.009.
[4]李如忠，钱家忠，汪家权．水污染物允许排放总量分配方法研究[J]．水利学报，2003,34(5):112-115.(LI R Z,QIAN J Z,WANG Jia-quan.Study on the total allowable discharge of water pollutants[J].Journal of Hydraulic Engineering,2003,34(5):112-115.(in Chinese))
[5]孟伟，张远，郑丙辉．水环境质量基准、标准与流域水污染物总量控制策略[J]．环境科学研究，2006,19(3):1-6.(MENG W,ZHANG Y,ZHENG B H.Water environment quality standards and total water pollutant control strategies[J].Research of Environmental Sciences,2006,19(3):1-6.(in Chinese))DOI:10.13198/j.res.2006.03.7.mengw.001.
[6] JOSHI V,MODAK P.Heuristic algorithms for waste load allocation in a river basin[J].Water Pollution Research&Control Brighton,1988,21(8-9):1057-1064.
[7]郝信东．基于信息熵的水污染物总量分配与控制策略研究[D]．天津：天津大学，2010.(HAO X D.Research on total water pollutant allocation and control strategy based on information entropy[D].Tianjin:Tianjin university,2010.(in Chinese))
[8]郝韶楠，李叙勇，江燕，等．水污染负荷优化分配模型研究进展[J]．环境科学与技术，2016,39(1):1-6.(HAO S N,LI X Y,JIANG Y,et al.Research progress on optimal allocation model of water pollution load[J].Environmental Science&Technology,2016,39(1):1-6.(in Chinese))DOI:10.3969/j.issn.1003-6504.2016.01.001.
[9]单保庆，王超，李叙勇，等．基于水质目标管理的河流治理方案制定方法及其案例研究[J]．环境科学学报，2015,35(8):2314-2323.(SHAN B Q,WANG C,LI X Y,et al.A case study of river management scheme formulation based on water quality objective management[J].Acta Scientiae Circumstantiae,2015,35(8):2314-2323.(in Chinese))DOI:10.13671/j.hjkxxb.2015.0029.
[10] NIKOO M R,KERACHIAN R,KARIMI A,et al.Optimal water and waste-load allocations in rivers using a fuzzy transformation technique:A case study.[J].Environmental Monitoring&Assessment,2013,185(3):2483-2502.DOI:10.1007/s10661-012-2726-6.
[11]李如忠，舒坤．基于多目标决策的水污染负荷分配方法[J]．环境科学学报，2011,31(12):2814-2821.(LI R Z,SHU K.Water pollution load distribution method based on multi-objective decision[J].Acta Scientiae Circumstantiae,2011,31(12):2814-2821.(in Chinese))DOI:10.13671/j.hjkxxb.2011.12.031.
[12] MOSTAFAVI S A,AFSHAR A.Waste load allocation using non-dominated archiving multi-colony ant algorithm[J].Procedia Computer Science,2011,3(none):64-69.
[13] SAADATPOUR M,AFSHAR A.Waste load allocation modeling with fuzzy goals;simulation-optimization approach[J].Water Resources Management,2007,21(7):1207-1224.DOI:10.1007/s11269-006-9077-4.
[14] LIANG S,JIA H,YANG C,et al.A Pollutant load hierarchical allocation method integrated in an environmental capacity management system for Zhushan Bay,Taihu Lake[J].Science of the Total Environment,2015,533:223-237.DOI:10.1016/j.scitotenv.2015.06.116.
[15]李如忠，舒琨．1526基于基尼系数的水污染负荷分配模糊优化决策模型[J]．环境科学学报，2010,30(7):1518-1526.(LI R Z,SHU K.A fuzzy optimization decision model for water pollution load distribution based on gini coefficient[J]. Acta Scientiae Circumstantiae,2010,30(7):1518-1526.(in Chinese))DOI:10.13671/j.hjkxxb.2010.07.022.
[16]秦迪岚，韦安磊，卢少勇．基于环境基尼系数的洞庭湖区水污染总量分配[J]．环境科学研究，2013,26(1):8-15.(QIN D L,WEI A L,LU S Y.Total water pollution distribution in dongting lake area based on environmental gini coefficient[J].Research of Environmental Sciences,2013,26(1):8-15.(in Chinese))DOI:10.13198/j.res.2013.01.12.qindl.006.
[17]杨芳．基于基尼系数法的九龙江流域水污染物排放总量分配研究[J]．环境科学与管理，2012,37(5):30-35.(YANG F.Research on total emission allocation of water pollutants in jiulong river basin based on gini coefficient[J].Environmental Science and Management,2012,37(5):30-35.(in Chinese))
[18]李泽琪，张玥，王晓燕，等．基于不同层级排污单元的水污染负荷分配方法[J]．资源科学，2018,40(7):1429-1437.(LI Z Q,ZHANG Y,WANG X Y,et al.Water pollution load distribution method based on different levels of pollutant discharge units[J].Resources science,2018,40(7):1429-1437.(in Chinese))DOI:10.18402/resci.2018.07.11.
[19]赵雪霞，于鲁冀，王燕鹏．清潩河流域（许昌段）水生态环境功能分区指标体系构建[J]．水利水电技术，2018,49(9):165-172.(YU L J ZHAO X X,WANG Y P.Qingyi river basin(Xuchang)water environment functional zoning index system to build[J].Water Resources and Hydropower Engineering,2018,49(9):165-172.(in Chinese))DOI:10.13928/j.cnki.wrahe.2018.09.022.
[20]田平，方晓波，王飞儿，等．基于环境基尼系数最小化模型的水污染物总量分配优化———以张家港平原水网区为例[J]．中国环境科学，2014,34(3):801-809.(TIAN P,FANG X B,WANG F E,et al.Optimization of total water pollutant distribution based on environmental gini coefficient minimization model—a case study of water network area in zhangjiagang plain[J].China Environmental Science,2014,34(3):801-809.(in Chinese))
[21] BOSI S,SEEGMULLER T.Optimal cycles and social inequality:what do we learn from the gini index[J].Research in Economics,2006,60(1):35-46.DOI:10.1016/j.rie.2005.12.003.
[22]王媛，牛志广，王伟．基尼系数法在水污染物总量区域分配中的应用[J]．中国人口·资源与环境，2008,18(3):177-180.(WANG Y,NIU Z G,WANG W.Application of gini coefficient method in regional distribution of total water pollutants[J].China Population Resources and Environment,2008,18(3):177-180).
[23]赵骞．河流和海洋污染物总量分配研究述评[J]．中国人口·资源与环境，2014,24(3):82-86.(ZHAO Q.Review of studies on total pollutant distribution in rivers and oceans[J].China Population Resources and Environment,2014,24(3):82-86.(in Chinese))DOI:10.12062/cpre.20170307.
[24]何帮强，洪兴建．基尼系数计算与分解方法研究综述[J]．统计与决策，2016(14):13-17.(HE B Q,HONG X J.Research review on gini coefficient calculation and decomposition methods[J].Statistics&Decision,2016(14):13-17.(in Chinese))DOI:10.13546/j.cnki.tjyjc.2016.14.003.
[25] CUTLER A,CUTLER D R,STEVENS J R.Random forests[J].Machine Learning,2004,45(1):157-176.DOI:10.1007/978-1-4419-9326-7_5.
[26] BIFET A,HOLMES G,PFAHRINGER B.Leveraging bagging for evolving data streams[C].European Conference on Machine Learning&Knowledge Discovery in Databases,2010.DOI:10.1007/978-3-642-15880-3_15.
[27]冯盼峰，温永仙．基于随机森林算法的两阶段变量选择研究[J]．系统科学与数学，2018,38(1):119-130.(FENG P F,WEN Y X.Two-stage variable selection based on random forest algorithm[J].Journal of Systems Science and Mathematical Sciences,2018,38(1):119-130.(in Chinese))
[28]方匡南，吴见彬，朱建平，等．随机森林方法研究综述[J]．统计与信息论坛，2011,26(3):32-38.(FANG K N,WU J B,ZHU J P,et al.Research review of random forest method[J].Statistics&Information Forum,2011,26(3):32-38.(in Chinese))
[29] RAMN DAZ-URIARTE,SARA ALVAREZ DE ANDRS.Gene selection and classification of microarray data using random forest[J].Bmc Bioinformatics,2006,7(1):3-0.DOI:10.1186/1471-2105-7-3.
[30] GREGORUTTI B,MICHEL B,SAINT-PIERRE P.Correlation and variable importance in random forests[J].Statistics and Computing,2017,27(3):659-678.DOI:10.1007/s11222-016-9646-1.
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