基于PSO-PPE模型的黑龙江省水资源生态安全探析
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摘要
针对黑龙江省水资源生态环境现状,构建以水资源、社会经济、生态环境为子系统的水资源生态安全评价指标体系。运用粒子群算法(PSO)优化投影寻踪评价模型(PPE),对黑龙江省水资源生态安全进行研究。结果表明,该地区2003—2016年水资源生态安全整体呈现上升趋势。水资源污染指数和工业万元产值耗水量等指标状态的极大改善,使得2009、2013年水资源生态安全程度分别较前一年增加67.16%和98.76%。2003—2016年水资源、生态环境系统发展波动较大,前者略有降低,后者上升,社会经济系统(权重为0.426 5)发展最为迅速且对水资源生态安全影响最显著。对水资源生态安全影响最显著的指标有人均湿地面积、农业万元产值耗水量、水资源污染指数、工业用水比例、工业万元产值耗水量。该评价结果可为当地政府及环保部门制定政策提供理论参考。
According to the current situation of water resources ecological environment in Heilongjiang Province, a water resources ecological security evaluation index system based on water resources, social economy and ecological environment was constructed. The particle swarm optimization algorithm is used to optimize the projection pursuit evaluation model, which is adopted to study the ecological security of Heilongjiang Province water resources. The results show that the overall ecological security of water resources in the region is on the rise from 2003 to 2016. In 2009 and 2013, the ecological security level of water resources increased by 67.16% and 98.76% compared with the previous year, respectively. Mainly due to the water pollution index, the industrial water consumption per 10 thousand RMB output and other indicators were greatly improved. During 2003-2016, the fluctuation of water resources and ecological environment subsystem development is bigger, the former is slightly lower, the latter rise, development of social economy subsystem(0.4265) is the most quickly and has the most significant impact on the water resources ecological security. The most significant indicators influencing the ecological security of water resources are: per capita wetland area, the agricultural water consumption per 10 thousand RMB output, water pollution index, industrial water consumption ratio, and the industrial water consumption per 10 thousand RMB output. The evaluation results can provide theoretical basis for local government and environmental protection departments to formulate policies.
According to the current situation of water resources ecological environment in Heilongjiang Province, a water resources ecological security evaluation index system based on water resources, social economy and ecological environment was constructed. The particle swarm optimization algorithm is used to optimize the projection pursuit evaluation model, which is adopted to study the ecological security of Heilongjiang Province water resources. The results show that the overall ecological security of water resources in the region is on the rise from 2003 to 2016. In 2009 and 2013, the ecological security level of water resources increased by 67.16% and 98.76% compared with the previous year, respectively. Mainly due to the water pollution index, the industrial water consumption per 10 thousand RMB output and other indicators were greatly improved. During 2003-2016, the fluctuation of water resources and ecological environment subsystem development is bigger, the former is slightly lower, the latter rise, development of social economy subsystem(0.4265) is the most quickly and has the most significant impact on the water resources ecological security. The most significant indicators influencing the ecological security of water resources are: per capita wetland area, the agricultural water consumption per 10 thousand RMB output, water pollution index, industrial water consumption ratio, and the industrial water consumption per 10 thousand RMB output. The evaluation results can provide theoretical basis for local government and environmental protection departments to formulate policies.
引文
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[4] WANG J Y,ZHAI Q W,QIAN G,et al.Study on Water Environmental Carrying Capacity Evaluation in Taihu Lake Basin[J].China Environmental Science,2017,37(5):1 979-1 987.
[5] REN Y T,YAO J P,XU D Y,et al.A Comprehensive Evaluation of Regional Water Safety Systems Based on a Similarity Cloud Model[J].Water Science & Technology,2017,76(3):594.
[6] 李云玲,郭旭宁,郭东阳,等.水资源承载能力评价方法研究及应用[J].地理科学进展,2017,36(3):342-349.LI Yunling,GUO Xuning,GUO Dongyang,et al.Research and Application of Water Resources Carrying Capacity Evaluation Method[J].Progress in Geography,2017,36(3):342-349.
[7] XUN Y,ZHANG X J,CHEN C L,et al.Comprehensive Evaluation of Soil Near Uranium Tailings,Beishan City,China[J].Bulletin of Environmental Contamination & Toxicology,2018(1):1-6.
[8] LIU X Z,LI W C,ZHAO Z Y,et al.Evaluation of Agricultural Water Resources Utilization Efficiency in Ningxia Based on Projection Pursuit[J].Water Saving Irrigation,2017(11):46-51.
[9] LIU H B,LIU Y F,LI L J,et al.Study of an Evaluation Method for the Water Resources Carrying Capacity Based on the Projection Pursuit Technique[J].Water Science & Technology Water Supply,2017,17(5):1 306-1 315.
[10] LIU D,ZHAO D,LIANG X,et al.Research on Evaluating Water Resource Resilience Based on Projection Pursuit Classification Model[J].Applied Water Science,2016,6(1):97-105.
[11] WANG S J,ZHANG X L.Projection Pursuit Dynamic Cluster Model and Its Application to Water Resources Carrying Capacity Evaluation[J].Journal of Water Resource & Protection,2010,2(5):449-454.
[12] LIU D,LIU C L,FU Q,et al.Projection Pursuit Evaluation Model of Regional Surface Water Environment Based on Improved Chicken Swarm Optimization Algorithm[J].Water Resources Management,2018,32(4):1 325-1 342.
[13] JAMRUS T,CHIEN C F,GEN M,et al.Hybrid Particle Swarm Optimization Combined with Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Orocessing Time for Semiconductor Manufacturing[J].IEEE Transactions on Semiconductor Manufacturing,2017(99):1.
[14] VOTI R L.Optimization of a Perfect Absorber Multilayer Structure by Genetic Algorithms[J].Journal of the European Optical Society-Rapid Publications,2018,14(1):11.
[15] WANG D S,TAN D P,LIU L.Particle Swarm Optimization Algorithm:An Overview[J].Soft Computing,2018,22(2):387-408.
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[17] 葛慧玲,焦扬,任永泰.哈尔滨市地下水水位预警模型[J].东北农业大学学报,2011,42(2):77-83.GE Huiling,JIAO Yang,REN Yongtai.Groundwater Level Warning Model of Harbin City[J].Journal of Northeast Agricultural University,2011,42(2):77-83.