跨界河流水污染应急决策支持系统研究
|
摘要
与非跨界河流污染相比,跨界河流突发性水污染事故普遍存在特征污染物不易光解、降解,污染强度大、跨度长,波及范围广、时间久的特点。针对我国跨界河流污染风险管理与控制实践中存在的问题,在对国内外典型流域跨界环境污染案例与环境风险管理机制调研的基础上,以典型跨界流域(跨省、国界)松花江流域为研究对象,开展了跨界河流水污染应急决策支持系统研究。
将跨界河流污染划分为常规污染与非常规突发污染,基于数据驱动模型研究河流常规污染预测技术,应用MATLAB软件建立了基于人工神经网络(ANN)技术的常规水质预测模型,通过对模型近期和远期预测数据进行分析得到:近期平均预测误差是3.82%,远期平均预测误差是4.74%,即模型对近期的预测效果要明显好于对远期水质的预测效果。对于突发污染高浓度污染团长时间、大尺度传输问题,基于机理模型—地表水模拟系统(Surface Water Modeling System,SMS)推演污染演替过程。同时基于不确定性原理构建二维随机水质模型,进一步研究数据稀缺条件下突发水污染事故污染带尺寸和目标断面浓度时间线的预测与分析。利用相关实测数据采用确定性和不确定性方法分别对模型进行了参数率定,得到了横向与纵向传质系数的概率分布和最大频率;污染带长度和宽度的模拟验证的平均误差小于15%,表明模型可较准确模拟计算河流污染带。
针对环境监管部门对跨界污染事件应急处置技术预案缺乏科学有效评估方法问题,和对国内外跨界水环境污染事件进行分类梳理基础上,设计构建了基于案例与规则联合推理的跨界环境污染应急预案库。利用层次分析法(AHP)建立案例评价指标体系,计算指标权重,提出包括问题描述、解决方案和结果评价框架的案例表示标准模型,比较分析了常用的数学算法,采用改进的欧氏距离法用于案例匹配以消除指标间数量级差异引起的计算问题,并利用决策树方法建立规则推理机制以完善预案生成过程。最后基于盲数理论建立预案可信度评价算法,并对松花江硝基苯污染案例进行验证。
基于博弈理论对跨界河流污染过程中,地方政府间竞争与合作这一不完全信息动态博弈过程进行分析,辨识与定义决策过程中的博弈要素,构建博弈双方收益矩阵,遵从纳什均衡概念寻求群体决策策略均衡点,建立跨界流域污染群体决策模式。基于“情景-应对”型应急管理理论与多智能体(Multi-Agent)技术研究了跨界风险协同决策机理,将流域上下游众多部门视为独立的智能体(Agent),采用带权分配的合同网协议(CNP)作为Agent间的协作机制,建立多智能体协同决策模型。将流域跨界污染问题视为复杂问题求解过程,进行分解和重构的过程,基于分布式并行处理的设计思想,由传统的群体一致性算法处理每个子问题的结果,消解多智能体间子预案冲突问题。
集成人工智能和软件工程技术,基于WebGIS技术建立了跨界河流水污染应急决策支持系统,并设计了相关数据库,以2005年松花江流域硝基苯污染事件为例,反演历史污染案例,验证河流长程污染模拟、快速预案生成及决策会商模块,实现了环境风险信息的通报与共享、流域水污染事故的应急响应处理与法律法规查询等功能,为有效控制松花江流域跨界污染提供群体决策支持。
Compared with normal river pollution, the pollutions, in the sudden water pollution accidents of trans-boundary river, have the characteristics: photolysis-resistent and biodegradation-resistent, heavy damage, long duration and large ranges. Due to the problems in the practice of risk management and control of trans-boundary river pollution, 362 environmental pollution incidents of past five years have been collected, especially the four incidents have been emphasized on, the water pollution incident in Songhua River in 2005 and in 2010, the two water pollution incident over the boundary of Jiangsu and Shandong in 2009, the domestic and foreign typical watershed cross-border environment pollution case and environmental risk management mechanism are research basis, typical trans-boundary basin (inter-provincial, borders) the songhua river basin are the research object we studied on trans-boundary river pollution emergency decision support system technology.
Trans-boundary river pollution was divided into conventional pollution and unconventional sudden pollution. Conventional pollution prediction technology was studied based on data-driven module, applying MATLAB to establish conventional water quality prediction model, Through recent and long-term prediction model analysis of the data available, it conclude that recent average prediction error is 3.82%, long-term average prediction error is 4.74%, which means forecast effect of recent significantly better than that of long-term. In order to predict high concentration pollution plume during emergency with long-time transport and large-scale transmission, the process was modeled by SMS(Surface Water Modeling System) based on mechanism-driven module, meanwhile, constructing two-dimensional random water quality model based on uncertainty principle to research sudden water pollution accidents with data scarce condition, and slick size and concentration of the time line target section was computational predicted and analyzed. By using the related experimental data validate the model parameters set by the means of certainty and uncertainty method separately, obtained that the rate of transverse and longitudinal mass transfer coefficient probability distribution and maximum frequency, Slick length and width of the average error of the analog verification less than 15%, indicating that the model can accurately simulated calculation the polluted plume.
To solve the problem of environmental authorities, for the lack of scientific and effective technical assessment on trans-boundary pollution incident emergency plans, Casebase based on CBR/RBR was designed on the basis of analyzing trans-boundary water environmental pollution events happened in domestic and abroad. Using the analytic hierarchy process (AHP) to build case evaluation index system, calculation index weight and proposed including problem description, solutions and results evaluation framework. Comparing and analyzing common mathematical algorithm and the standard model, adopting an improved Euclidean distance method to eliminate index for case matching between the magnitude of differences cause, using decision tree method to establish rule reasoning mechanism to perfect plan formation process. Based on blind number theory, established the credibility assessment algorithm of the plan and the case of nitrobenzene pollution of Songhua river was used for verification.
Non-complete information dynamic game process between the local governments was analyzed based on game theory during the pollution happened in in the trans-boundary basin. Identification and definition of the game elements in decision process was made and return matrix of the game was also constructed. Following Nash equilibrium concept for group decision strategy equilibrium, establishing trans-boundary basin pollution group decision model.
Based on the scenario-responses emergency management theory and of multi-Agent technology, the collaborative decision-making mechanism in trans- boundary emergency was researched and multitudinous departments in upstream and downstream of river basin was designed as an Agent, modified contract nets protocol (CNP) was chosen as Agent collaboration mechanism, at last, collaborative decision- making of multi-Agent system was established based on the thought of distributed parallel processing design. The conflicts of Agents was solved through the traditional group consistency algorithm processing.
Finally, integrating artificial intelligence and software engineering technology, water pollution emergency decision support system and its database and case base was constructed based on WebGIS technology. Case of nitrobenzene pollution happened in Songhua river basin, 2005 was chosen as inversion case to validate river’s long-range pollution simulating, rapid preplan generation and decision-making consultation. The function of environmental risk information bulletin and sharing, water pollution emergency response, laws and regulations inquires was realized for the purpose of effective control of the pollution of Songhua river basin and it provides group decision support for trans-boundary pollution.
将跨界河流污染划分为常规污染与非常规突发污染,基于数据驱动模型研究河流常规污染预测技术,应用MATLAB软件建立了基于人工神经网络(ANN)技术的常规水质预测模型,通过对模型近期和远期预测数据进行分析得到:近期平均预测误差是3.82%,远期平均预测误差是4.74%,即模型对近期的预测效果要明显好于对远期水质的预测效果。对于突发污染高浓度污染团长时间、大尺度传输问题,基于机理模型—地表水模拟系统(Surface Water Modeling System,SMS)推演污染演替过程。同时基于不确定性原理构建二维随机水质模型,进一步研究数据稀缺条件下突发水污染事故污染带尺寸和目标断面浓度时间线的预测与分析。利用相关实测数据采用确定性和不确定性方法分别对模型进行了参数率定,得到了横向与纵向传质系数的概率分布和最大频率;污染带长度和宽度的模拟验证的平均误差小于15%,表明模型可较准确模拟计算河流污染带。
针对环境监管部门对跨界污染事件应急处置技术预案缺乏科学有效评估方法问题,和对国内外跨界水环境污染事件进行分类梳理基础上,设计构建了基于案例与规则联合推理的跨界环境污染应急预案库。利用层次分析法(AHP)建立案例评价指标体系,计算指标权重,提出包括问题描述、解决方案和结果评价框架的案例表示标准模型,比较分析了常用的数学算法,采用改进的欧氏距离法用于案例匹配以消除指标间数量级差异引起的计算问题,并利用决策树方法建立规则推理机制以完善预案生成过程。最后基于盲数理论建立预案可信度评价算法,并对松花江硝基苯污染案例进行验证。
基于博弈理论对跨界河流污染过程中,地方政府间竞争与合作这一不完全信息动态博弈过程进行分析,辨识与定义决策过程中的博弈要素,构建博弈双方收益矩阵,遵从纳什均衡概念寻求群体决策策略均衡点,建立跨界流域污染群体决策模式。基于“情景-应对”型应急管理理论与多智能体(Multi-Agent)技术研究了跨界风险协同决策机理,将流域上下游众多部门视为独立的智能体(Agent),采用带权分配的合同网协议(CNP)作为Agent间的协作机制,建立多智能体协同决策模型。将流域跨界污染问题视为复杂问题求解过程,进行分解和重构的过程,基于分布式并行处理的设计思想,由传统的群体一致性算法处理每个子问题的结果,消解多智能体间子预案冲突问题。
集成人工智能和软件工程技术,基于WebGIS技术建立了跨界河流水污染应急决策支持系统,并设计了相关数据库,以2005年松花江流域硝基苯污染事件为例,反演历史污染案例,验证河流长程污染模拟、快速预案生成及决策会商模块,实现了环境风险信息的通报与共享、流域水污染事故的应急响应处理与法律法规查询等功能,为有效控制松花江流域跨界污染提供群体决策支持。
Compared with normal river pollution, the pollutions, in the sudden water pollution accidents of trans-boundary river, have the characteristics: photolysis-resistent and biodegradation-resistent, heavy damage, long duration and large ranges. Due to the problems in the practice of risk management and control of trans-boundary river pollution, 362 environmental pollution incidents of past five years have been collected, especially the four incidents have been emphasized on, the water pollution incident in Songhua River in 2005 and in 2010, the two water pollution incident over the boundary of Jiangsu and Shandong in 2009, the domestic and foreign typical watershed cross-border environment pollution case and environmental risk management mechanism are research basis, typical trans-boundary basin (inter-provincial, borders) the songhua river basin are the research object we studied on trans-boundary river pollution emergency decision support system technology.
Trans-boundary river pollution was divided into conventional pollution and unconventional sudden pollution. Conventional pollution prediction technology was studied based on data-driven module, applying MATLAB to establish conventional water quality prediction model, Through recent and long-term prediction model analysis of the data available, it conclude that recent average prediction error is 3.82%, long-term average prediction error is 4.74%, which means forecast effect of recent significantly better than that of long-term. In order to predict high concentration pollution plume during emergency with long-time transport and large-scale transmission, the process was modeled by SMS(Surface Water Modeling System) based on mechanism-driven module, meanwhile, constructing two-dimensional random water quality model based on uncertainty principle to research sudden water pollution accidents with data scarce condition, and slick size and concentration of the time line target section was computational predicted and analyzed. By using the related experimental data validate the model parameters set by the means of certainty and uncertainty method separately, obtained that the rate of transverse and longitudinal mass transfer coefficient probability distribution and maximum frequency, Slick length and width of the average error of the analog verification less than 15%, indicating that the model can accurately simulated calculation the polluted plume.
To solve the problem of environmental authorities, for the lack of scientific and effective technical assessment on trans-boundary pollution incident emergency plans, Casebase based on CBR/RBR was designed on the basis of analyzing trans-boundary water environmental pollution events happened in domestic and abroad. Using the analytic hierarchy process (AHP) to build case evaluation index system, calculation index weight and proposed including problem description, solutions and results evaluation framework. Comparing and analyzing common mathematical algorithm and the standard model, adopting an improved Euclidean distance method to eliminate index for case matching between the magnitude of differences cause, using decision tree method to establish rule reasoning mechanism to perfect plan formation process. Based on blind number theory, established the credibility assessment algorithm of the plan and the case of nitrobenzene pollution of Songhua river was used for verification.
Non-complete information dynamic game process between the local governments was analyzed based on game theory during the pollution happened in in the trans-boundary basin. Identification and definition of the game elements in decision process was made and return matrix of the game was also constructed. Following Nash equilibrium concept for group decision strategy equilibrium, establishing trans-boundary basin pollution group decision model.
Based on the scenario-responses emergency management theory and of multi-Agent technology, the collaborative decision-making mechanism in trans- boundary emergency was researched and multitudinous departments in upstream and downstream of river basin was designed as an Agent, modified contract nets protocol (CNP) was chosen as Agent collaboration mechanism, at last, collaborative decision- making of multi-Agent system was established based on the thought of distributed parallel processing design. The conflicts of Agents was solved through the traditional group consistency algorithm processing.
Finally, integrating artificial intelligence and software engineering technology, water pollution emergency decision support system and its database and case base was constructed based on WebGIS technology. Case of nitrobenzene pollution happened in Songhua river basin, 2005 was chosen as inversion case to validate river’s long-range pollution simulating, rapid preplan generation and decision-making consultation. The function of environmental risk information bulletin and sharing, water pollution emergency response, laws and regulations inquires was realized for the purpose of effective control of the pollution of Songhua river basin and it provides group decision support for trans-boundary pollution.
引文
[1]陈占锋,谢一帆.突发环境事件下的环境风险治理研究[J].合肥工业大学学报(社会科学版),2007(21):90-93.
[2]罗朝春,涂光瑜,罗毅,盛戈暤，吴军.基于多Agent的电力市场技术支持系统研究[J].电力系统及其自动化学报,2005,3(17):1-5
[3] Pervan G P. A Review of Research in Group Support Systems: Leaders, Approaches and Directions[J]. Decision Support Systems, 1998, 23(2):149-159.
[4]陈晓红,徐选华.决策应用软件开发平台Smart Decision研制——国家杰出青年科学基金项目回溯[J].管理学报,2006,3(3):253-257.
[5]张慧勤,高树婷,王秋玲,等.国家环境宏观决策支持系统的研究[J].环境科学研究,1991,4(4):57-64.
[6]罗宏,吕连宏. EDSS及其在EIA中的应用[J].环境科学研究,2006,19(3):139-144.
[7]陈明,王道尚,张丙珍.综合防控重金属污染,保障群众生命安全——2009年典型重金属污染事件解析[J].环境保护,2010(3):49-51.
[8]陈英.突发性环境污染事故应急监测预案的研究[D].江苏大学硕士学位论文,2007:4-5.
[9]翟平阳.松花江流域水质污染的防治[J].北方环境,2003(1):12-14.
[10] National Response Team. National Oil and Hazardous Substances Pollution Contingency Plan. http://www.nrt.org/, 1994.
[11] Yu Yan. Introduction to Hazardous Material Emergeney Response System in Overseas[J]. Safety Journal, 2002(2): 42-44.
[12]赵起越,白俊松.国内外环境应急监测技术现状及发展[J].安全与环境工程,2006(3): 13-15.
[13] Barletta R. An Introduction to Case Based Reasoning[J]. AI Expert, 1991(8): 43-49.
[14] Kolodner J L. An Introduction to Case-based Reasoning[J]. Artificial Intelligence Review, 1992, 6(1): 3-34.
[15] Peng G L. Applying RBR and CBR to Develop a VR Based Integrated System for Machining Fixture Design. Expert Systems with Applications, vol. 38, 2011:26-38.
[16] Xu B and Liu L Q. Research of Litchi Diseases Diagnosis Expert System Based on Rbr and Cbr. Computer and Computing Technologies in Agriculture Ii, Vol 1, vol. 293, 2009:681-688.
[17] Yuan Fong-ching, Chiu Chao-chang. A hierarchical Design of Case-basedReasoning in the Balanced Scorecard Application[J]. Expert Systems with Applications, 2009(36): 333–342.
[18]罗杰文,施智平,何清.一种CBR与RBR相结合的快速预案生成系统[J].计算机研究与发展,2007,44(4):660-666.
[19]张建华,刘仲英.案例推理和规则推理结合的紧急预案信息系统[J].同济大学学报,2002,30(7):890.
[20] Yang Li-feng, Kou Zi-ming. Analysis and Construction of the Emergency Deciision Support System for Serious Environmental Pollution Accitent In Coal Chemical Industry Region[C]. ISECS International Colloquium on Computing,Communication,Control,and Management(CCCM), 2010:597.
[21] Gabel T and Riedmiller M. CBR for State Value Function Approximation in Reinforcement Learning. Case-Based Reasoning Research and Development, Proceedings, vol. 3620, 2005: 206-221.
[22]罗忠良,王克运,康仁科.基于案例推理系统中案例检索算法的探索[J].计算机工程与应用,2005,03(25):230-232.
[23]唐华松,姚耀文.数据挖掘中决策树算法的探讨[J].计算机应用研究,2001(8):18-22.
[24] Ye, L, Lu R Z, et al. (2009). Application of Decision-tree Algorithm to GSP Analysis and Assessment of Drug Safety Situation. 2009 Second InternationalSymposium on Knowledge Acquisition and Modeling: Kam 2009, Vol 2: 103-1060-409.
[25]刘开第.不确定性信息数学处理及应用[M].科学出版社,北京,1999:160-163.
[26]李如忠,钱家忠,汪家权.基于盲数的水质综合指数评价模型[J].中国农村水利水电,2008(6):19-22.
[27]王春燕.基于盲数回归法的港口吞吐量预测[J].交通标准化, 2006(9):167-168.
[28]常勇,束洪春,朱文涛.电网规划的负荷预测盲数建模方法研究[J].云南水力发电,2007,4(23):1-4.
[29]陈守煜.工程可变模糊集理论与模型—模糊水文水资源学数学基础[J],大连理工大学学报,2005,45(2):308-312.
[30] Liu Kai-di, Wu He-qin, Pang Yan-jun. MAMB Model of Comprehensive Treatment for the Experts’Opinions [J]. Mathematics in Practice and Theory, 2001, 31(4): 474-477.
[31] Georgakarakos S, Koutsoubas D and Valavanis V .Time Series Analysis and Forecasting Techniques Applied on Loliginid and Ommastrephid Landings in Greek Waters[J]. Fisheries Research, 2006, 78(1):55-71.
[32] Kumaresan M and Riyazuddin P. Factor Analysis and Linear Regression Model (LRM) of Metal Speciation and Physico-chemical Characters of Groundwater Samples. Environmental Monitoring and Assessment, vol. 138, 2008: 65-79.
[33] Pai T Y, Lin K L, et al. Predicting the Comelting Temperatures of Municipal Solid Waste Incinerator Fly Ash and Sewage Sludge Ash Using Grey Model and Neural Network. Waste Management and Research, 2011, 29(3): 284-293.
[34] Zare A K, et al. Forecasting Nitrate Concentration in Groundwater Using Artificial Neural Network and Linear Regression Models. International Agrophysics, vol. 25, 2011: 187-192.
[35] Migliaccio K W, Chaubey I, Haggard B E. Evaluation of UncertaintyPropagation into River Water Quality Predictions to Guide Future Monitoring Campaigns[J]. Environmental Modeling and Software, 2007, 22(7): 987-999.
[36] Ahmet K, Kadri Y, Osman C. Performance of Two Stochastic Approaches for Forecasting Water Quality and Stream Flow Data from Ye?iιlrmak River Turkey[J]. Environmental Modelling and Software, 2005, 20(9): 1195-1200.
[37] Robert J M, Graeme C D, Holger R M, John B N. Application of Partial Mutual Information Variable Selection to ANN Forecasting of Water Quality in Water Distribution Systems[J]. Environmental Modelling and Software, 2008, 23(10-11): 1289-1299.
[38]彭放等.数学建模方法[M].北京:科学出版社,2007:137-138.
[39] Gallant S I. Neural Network Learning and Expert Systems[M]. The MIT press, Massachusetts,USA, 1993.
[40] Smith M. Neural Networks for Statistical Modelling[M]. Van Nostrand Reinhold, NY, 1994:235.
[41] Kunwar P S, Ankita B, Amrita M, Gunja J. Artificial Neural Network Modeling of the River Water Quality—A Case Study[J]. Ecological Modelling, 2009(220): 888–895.
[42] Holger R M, Ashu J, Graeme C D. Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions[J]. Environmental Modelling and Software, 2010(25): 891-909.
[43] Gardner M W, Dorling S R. Artificial Neural Network: the Multilayer Perceptron: a Review of Applications in Atmospheric Sciences[J]. Atmos. Environ, 1998(32): 2627–2636.
[44]孙琳琳.基于人工智能方法的长春新立城水库水质分析及富营养化趋势研究[D].东北师范大学硕士学位论文,2010.
[45] Rogers L L, Dowla F U. Optimization of Groundwater Remediation UsingArtificial Neural Networks with Parallel Solute Transport Modeling. Water Resour. Res. 1994, 30: 457–481.
[46] Raman H, Chandramouli V. Deriving a General Operating Policy for Reservoirs Using Neural Networks[J]. Water Resour. Plann. Manage. 1996, 122: 342–347.
[47] Wen C W, Lee C S. A Neural Network Approach to Multiobjective Optimization for Water Quality Management in a River Basin[J]. Water Resour. Res. 1998, 34: 427–436.
[48] Lek S, Guegan J F. Artificial Neural Networks as a Tool in Ecological Modelling an Introduction[J]. Ecol. Model, 1999, 120: 65–73.
[49] Kuo Y, Liu C, Lin K H. Evaluation of the Ability of an Artificial Neural Network Model to Assess the Variation of Groundwater Quality in an Area of Blackfoot Disease in Taiwan[J]. Water Res, 2004, 38: 148–158.
[50]吴锋.基于AHP与CBR技术的注射模智能报价方法研究[D].浙江大学硕士学位论文,2008:30-35.
[51] Demigha S. The Retrieval Process in the SAFRS System with the Case-based Reasoning Approach. Iceis 2007: Proceedings of the Ninth International Conference on Enterprise Information Systems, 2007: 468-473--519.
[52]郭劲松,王红,龙腾锐.水环境质量的隶属度BP网络决策模型[J].中国给水排水,2000,16(3):9-11.
[53]龙腾锐,郭劲松,霍国友.水质综合评价的Hopfield网络模型[J].重庆建筑大学学报,2002,24(2):57-60.
[54] Hong S H, et al. Monitoring of Sequencing Batch Reactor for Nitrogen and Phosphorus Removal Using Neural Networks. Biochemical Engineering Journal, 2007. 35(3): 365～370.
[55] Sonil R I,侯德刚,张振家,周伟丽,迟莉娜.基于BP人工神经网络的生化处理水水质预测[J].现代化工,2009,29(12):66-70.
[56] Jia Chun-jiang, Chen Guang-xue, Li Xiao-zhou. Research on the MathematicalModel and Air-Drying Mechanism of Water-based Ink. Printing and Packaging Study, 2011, 174: 31-384.
[57]黄真理,李玉樑等.三峡水库水质预测和环境容量计算[M].北京:中国水利水电出版社,2006.1
[58]赵英.地表水源水质预测模型数据挖掘技术及其适用性研究[D].哈尔滨工业大学博士学位论文,2008.
[59] Franceschini, Samuela T, Christina W. Assessment of Uncertainty Sources in Water Quality Modeling in the Niagara River. Advances in Water Resources, 2010, 33(4):493-503.
[60]王祎.基于不确定性原理的二维随机水质模型及原型系统开发[D].哈尔滨:哈尔滨工业大学硕士学位论文,2009:19-24.
[61] Sprague R H. A Framework for the Development of Decision Support Systems[J]. Management Information Systems, 1980, 4:1-26.
[62] Haidary A, Nakane K. Sensitivity Analysis of Stream Water Quality and Land Cover Linkage Models Using Monte Carlo Method[J]. International Journal of Environmental Research, 2010, 4(1):121-130.
[63] Wang H Q. Improvement of the Recognition Probability about Camouflage Target Based on BP Neural Network[J]. Spectroscopy and Spectral Analysis, 2010, 30(12): 3316-3319.
[64] Sànchez M, Giberta K, Sojdad R S,et al. Chapter Eight Intelligent Environmental Decision Support Systems. Developments in Integrated Environmental Assessment, 2008, 3:119-144.
[65] Haseman W D. GPLAN: an Operational DSS[J]. Data Base, 1977, 8(3):15.
[66]钱程,姚瑶.情景分析法在黑龙江省水环境污染防治工作中的应用[J].环境科学与管理,2006,1:78-80.
[67]易征,李蜀庆,周丹丹.情景分析法在长江上游环境污染治理中的应用[J].环境科学与管理,2009,2:90-93.
[68] Lautenbach S, Berlekamp J, Graf N, et al. Scenario Analysis and ManagementOptions for Sustainable River Basin Management: Application of the Elbe DSS[J]. Environmental Modelling and Software, 2009, 24:26–43.
[69] Yoshimura C, Zhou M. 2020s Scenario Analysis of Nutrient Load in the Mekong River Basin Using a Distributed Hydrological Model[J]. Science of the Total Environment, 2009, 407:5356–5366.
[70] Jessel B, Jacob J. Land Use Scenario Development and Stakeholder Involvement as Tools for Watershed Management within the Havel River Basin[J]. Limnologica, 2005, 35:220–233.
[71] Wolter M H, Guercke. Multi-Agent Based Distributed Power Flow Calculation. Ieee Power and Energy Society General Meeting, 2010:6545.
[72]左春荣.基于定性推理理论和Multi-Agent的群决策支持系统研究[D].合肥:合肥工业大学博士学位论文,2008.
[73]詹伟,王兆红,邱菀华.基于Multi-Agent的分布式项目管理决策支持系统研究[J].计算机应用研究,2007:63-65.
[74]于卫红. RMI在海上搜救智能决策支持系统中的应用[J].计算机工程,2009,12(35):154-158.
[75]陈登科,张大发,江玮等.基于MAS的船用压水堆核事故应急决策支持系统[J].核动力工程,2007,5(28):79-82.
[76] Gonzalez-Velez H M. Health Agents: Distributed Multi-Agent Brain Tumor Diagnosis and Prognosis[J]. Applied Intelligence, 2009, 30(3):191-202.
[77]赵建军.基于多Agent的流域洪水预报系统研究与应用[D].西北农林科技大学硕士学位论文,2008:5.
[78]迟嘉昱.基于网格的开放式决策支持系统研究[D].湖北:华中科技大学硕士学位论文,2004:7.
[79]卿晓霞.基于Multi-Agent的分布式污水处理智能化系统研究[D].重庆:重庆大学博士学位论文,2007:1.
[80]许劲.基于传质原理的二维随机水质模型研究与应用[D].重庆:重庆大学博士学位论文,2007.
[81] Wang Y , Wang P, et al. Two-D Stochastic Water Quality Model Based on Mass Transfer Theory for Warning Emergency Water Pollution Accidents[J]. Progress in Environmental Science and Technology, 2009:1380-1384.
[82]张秋花,薛惠锋,吴介军.多智能体系统MAS及其应用[J].计算机仿真,2007,24(6):133-137.
[83] Shoham. Agent-oriented Programming[J]. Artificial Intelligence, 1993, 60(1):51-92.
[84]郭亮,王鹏,赵英.基于BP神经网络的松花江四方台水质预测[J].哈尔滨工业大学学报,2009,41(6):62-66.
[85]宋国君,谭炳卿等.中国淮河流域水环境保护政策评估[M].北京:中国人民大学出版社,2007年第1版,348-361.
[86] Tao J E. The Game Model Theory and Practice Analysis On Environmental Management. Enivironment Materials and Environment Management, 2010, 13 :1415-1418.
[87]许东,吴铮.基于MATLAB6.x的系统分析与设计——神经网络[M].第二版.西安:西安电子科技大学出版社,2002:19-24.
[88] Haidary A, Nakane K. Sensitivity Analysis of Stream Water Quality and Land Cover Linkage Models Using Monte Carlo Method[J]. International Journal of Environmental Research, 2010, 4(1):121-130.
[89] Miller K D, Waller H G, Scenarios. Real Options and Integrated Risk Management[J]. Long Range Planning, 2003, 36(1):93-107.
[90] Pai P F, Lee F C. A Rough Set Based Model in Water Quality Analysis[J]. Water Resources Management, 2010, 24(11):2405-2418.
[91] Yang S Y, Hu C L. An Ontological Proxy Agent with prediction, CBR and RBR Techniques for Fast Query Processing[J]. Expert Systems with Applications, 2009, 36(5):9358-9370.
[92] Chakravarthy V N. Advanced Medical Image Analysis using GIIM, IISM with CBR and RBR, Intelligence Proceedings of World Academy of Science[J].Engineering and Technology, 2007, 22:553-557.
[93] Jani H M, Peck L S. Applying Machine Learning Using Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) Approaches to Object-Oriented Application Framework Documentation[J]. Third International Conference on Information Technology and Applications, 2005(1): 52-57.
[94]姜继平.水污染危险区域判定及其原型系统开发[D].哈尔滨:哈尔滨工业大学硕士学位论文,2008.
[95] Guo P Y, Shi B Q, Xiao C Y, et al. Calculation Method of Mechanical Structure Complex Time-dependent Reliability with Blind Number Theory and Its Implementation[J]. Engineering and Digital Enterprise Technology, 2009, 16:816-820.
[96]田军,张朋柱,王刊良.基于德尔菲法的专家意见集成模型研究[J].系统工程理论与实践,2004,1:57-62.
[97]李如忠,洪天求,钱家忠.基于盲数的区域水资源可持续利用评价研究[J].中北大学学报(自然科学版),2006,5(27):436-441.
[98]张一喆，樊玉茹，李彩琴.松花江流域污染防治的科研工作[J].北方环境,2003(4):12-15.
[99] Trianni G F, Dell A. Geographic Information System (GIS) Aided Persegment Scene Analysis of Multitemporal Spaceborne Synthetic Aperture Radar (SAR) Series with Application to Urban Areas[J]. International Journal of Remote Sensing, 2010, 31(22): 6005-6014.
[100]Facchinei F, Fischer A, Piccialli V. Generalized Nash Equilibrium Problems and Newton Methods[J]. Mathematical Programming, 2009, 117:163-194.
[101]曾文慧.越界水污染规制——对中国跨行政区流域污染的考察[D].复旦大学博士学位论文,2005:2.
[102]Jeste D V, Ardelt M, Blazer D, et al. Expert Consensus on Characteristics of Wisdom: A Delphi Method Study[J]. Gerontologist, 2010, 50(5):668-680.
[103]Wang S C, Chen M K. A Hybrid Delphi-Bayesian Method to EstablishBusiness Data Integrity Policy A Benchmark Data Center Case Study[J]. Kybernents, 2010, 39(5):800-824.
[104]Hart M D. A Delphi Study to Determine Baseline Informatics Competencies for Nurse Managers[J]. Cin-Computers Informatics Nursing, 2010, 28(6): 364-370.
[105]Garrett Hardin. The Tragedy of the Commons[J]. Science, 1968, 162:1243-1248.
[106]Lu Y Q, He Y T. Research on Game Theory of Motivation and Mechanism of Cooperative Innovation in Multinational Technical Alliance[J]. Proceedings of the 1st International Conference on Sustainable Construction and Risk Management, 2010, 1: 1174-1178.
[107]Muller J P, Farolfi S, Bonte B. An Iterative Construction of Multi-agent Models to Represent Water Supply and Demand Dynamics at the Catchment Level[J]. Environmental Modelling and Software, 2010, 25(10):1130-1148.
[108]Feng X, Cao K, Ma H. Pinch Multi-agent Genetic Algorithm for Optimizing Water-using networks[J]. Computers and Chemical Engineering, 2007, 31(12): 1565-1575.
[109]林高松,李适宇,江峰.河流允许排污量公平分配的多准则决策方法[J].环境科学学报,2007,27(3):494-500.
[110]Manfred Hauswirth. A Component and Communication Model for Push Systems[C]. Proc. of the 7th European Software Engineering Conference, 1999:20-38.
[2]罗朝春,涂光瑜,罗毅,盛戈暤，吴军.基于多Agent的电力市场技术支持系统研究[J].电力系统及其自动化学报,2005,3(17):1-5
[3] Pervan G P. A Review of Research in Group Support Systems: Leaders, Approaches and Directions[J]. Decision Support Systems, 1998, 23(2):149-159.
[4]陈晓红,徐选华.决策应用软件开发平台Smart Decision研制——国家杰出青年科学基金项目回溯[J].管理学报,2006,3(3):253-257.
[5]张慧勤,高树婷,王秋玲,等.国家环境宏观决策支持系统的研究[J].环境科学研究,1991,4(4):57-64.
[6]罗宏,吕连宏. EDSS及其在EIA中的应用[J].环境科学研究,2006,19(3):139-144.
[7]陈明,王道尚,张丙珍.综合防控重金属污染,保障群众生命安全——2009年典型重金属污染事件解析[J].环境保护,2010(3):49-51.
[8]陈英.突发性环境污染事故应急监测预案的研究[D].江苏大学硕士学位论文,2007:4-5.
[9]翟平阳.松花江流域水质污染的防治[J].北方环境,2003(1):12-14.
[10] National Response Team. National Oil and Hazardous Substances Pollution Contingency Plan. http://www.nrt.org/, 1994.
[11] Yu Yan. Introduction to Hazardous Material Emergeney Response System in Overseas[J]. Safety Journal, 2002(2): 42-44.
[12]赵起越,白俊松.国内外环境应急监测技术现状及发展[J].安全与环境工程,2006(3): 13-15.
[13] Barletta R. An Introduction to Case Based Reasoning[J]. AI Expert, 1991(8): 43-49.
[14] Kolodner J L. An Introduction to Case-based Reasoning[J]. Artificial Intelligence Review, 1992, 6(1): 3-34.
[15] Peng G L. Applying RBR and CBR to Develop a VR Based Integrated System for Machining Fixture Design. Expert Systems with Applications, vol. 38, 2011:26-38.
[16] Xu B and Liu L Q. Research of Litchi Diseases Diagnosis Expert System Based on Rbr and Cbr. Computer and Computing Technologies in Agriculture Ii, Vol 1, vol. 293, 2009:681-688.
[17] Yuan Fong-ching, Chiu Chao-chang. A hierarchical Design of Case-basedReasoning in the Balanced Scorecard Application[J]. Expert Systems with Applications, 2009(36): 333–342.
[18]罗杰文,施智平,何清.一种CBR与RBR相结合的快速预案生成系统[J].计算机研究与发展,2007,44(4):660-666.
[19]张建华,刘仲英.案例推理和规则推理结合的紧急预案信息系统[J].同济大学学报,2002,30(7):890.
[20] Yang Li-feng, Kou Zi-ming. Analysis and Construction of the Emergency Deciision Support System for Serious Environmental Pollution Accitent In Coal Chemical Industry Region[C]. ISECS International Colloquium on Computing,Communication,Control,and Management(CCCM), 2010:597.
[21] Gabel T and Riedmiller M. CBR for State Value Function Approximation in Reinforcement Learning. Case-Based Reasoning Research and Development, Proceedings, vol. 3620, 2005: 206-221.
[22]罗忠良,王克运,康仁科.基于案例推理系统中案例检索算法的探索[J].计算机工程与应用,2005,03(25):230-232.
[23]唐华松,姚耀文.数据挖掘中决策树算法的探讨[J].计算机应用研究,2001(8):18-22.
[24] Ye, L, Lu R Z, et al. (2009). Application of Decision-tree Algorithm to GSP Analysis and Assessment of Drug Safety Situation. 2009 Second InternationalSymposium on Knowledge Acquisition and Modeling: Kam 2009, Vol 2: 103-1060-409.
[25]刘开第.不确定性信息数学处理及应用[M].科学出版社,北京,1999:160-163.
[26]李如忠,钱家忠,汪家权.基于盲数的水质综合指数评价模型[J].中国农村水利水电,2008(6):19-22.
[27]王春燕.基于盲数回归法的港口吞吐量预测[J].交通标准化, 2006(9):167-168.
[28]常勇,束洪春,朱文涛.电网规划的负荷预测盲数建模方法研究[J].云南水力发电,2007,4(23):1-4.
[29]陈守煜.工程可变模糊集理论与模型—模糊水文水资源学数学基础[J],大连理工大学学报,2005,45(2):308-312.
[30] Liu Kai-di, Wu He-qin, Pang Yan-jun. MAMB Model of Comprehensive Treatment for the Experts’Opinions [J]. Mathematics in Practice and Theory, 2001, 31(4): 474-477.
[31] Georgakarakos S, Koutsoubas D and Valavanis V .Time Series Analysis and Forecasting Techniques Applied on Loliginid and Ommastrephid Landings in Greek Waters[J]. Fisheries Research, 2006, 78(1):55-71.
[32] Kumaresan M and Riyazuddin P. Factor Analysis and Linear Regression Model (LRM) of Metal Speciation and Physico-chemical Characters of Groundwater Samples. Environmental Monitoring and Assessment, vol. 138, 2008: 65-79.
[33] Pai T Y, Lin K L, et al. Predicting the Comelting Temperatures of Municipal Solid Waste Incinerator Fly Ash and Sewage Sludge Ash Using Grey Model and Neural Network. Waste Management and Research, 2011, 29(3): 284-293.
[34] Zare A K, et al. Forecasting Nitrate Concentration in Groundwater Using Artificial Neural Network and Linear Regression Models. International Agrophysics, vol. 25, 2011: 187-192.
[35] Migliaccio K W, Chaubey I, Haggard B E. Evaluation of UncertaintyPropagation into River Water Quality Predictions to Guide Future Monitoring Campaigns[J]. Environmental Modeling and Software, 2007, 22(7): 987-999.
[36] Ahmet K, Kadri Y, Osman C. Performance of Two Stochastic Approaches for Forecasting Water Quality and Stream Flow Data from Ye?iιlrmak River Turkey[J]. Environmental Modelling and Software, 2005, 20(9): 1195-1200.
[37] Robert J M, Graeme C D, Holger R M, John B N. Application of Partial Mutual Information Variable Selection to ANN Forecasting of Water Quality in Water Distribution Systems[J]. Environmental Modelling and Software, 2008, 23(10-11): 1289-1299.
[38]彭放等.数学建模方法[M].北京:科学出版社,2007:137-138.
[39] Gallant S I. Neural Network Learning and Expert Systems[M]. The MIT press, Massachusetts,USA, 1993.
[40] Smith M. Neural Networks for Statistical Modelling[M]. Van Nostrand Reinhold, NY, 1994:235.
[41] Kunwar P S, Ankita B, Amrita M, Gunja J. Artificial Neural Network Modeling of the River Water Quality—A Case Study[J]. Ecological Modelling, 2009(220): 888–895.
[42] Holger R M, Ashu J, Graeme C D. Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions[J]. Environmental Modelling and Software, 2010(25): 891-909.
[43] Gardner M W, Dorling S R. Artificial Neural Network: the Multilayer Perceptron: a Review of Applications in Atmospheric Sciences[J]. Atmos. Environ, 1998(32): 2627–2636.
[44]孙琳琳.基于人工智能方法的长春新立城水库水质分析及富营养化趋势研究[D].东北师范大学硕士学位论文,2010.
[45] Rogers L L, Dowla F U. Optimization of Groundwater Remediation UsingArtificial Neural Networks with Parallel Solute Transport Modeling. Water Resour. Res. 1994, 30: 457–481.
[46] Raman H, Chandramouli V. Deriving a General Operating Policy for Reservoirs Using Neural Networks[J]. Water Resour. Plann. Manage. 1996, 122: 342–347.
[47] Wen C W, Lee C S. A Neural Network Approach to Multiobjective Optimization for Water Quality Management in a River Basin[J]. Water Resour. Res. 1998, 34: 427–436.
[48] Lek S, Guegan J F. Artificial Neural Networks as a Tool in Ecological Modelling an Introduction[J]. Ecol. Model, 1999, 120: 65–73.
[49] Kuo Y, Liu C, Lin K H. Evaluation of the Ability of an Artificial Neural Network Model to Assess the Variation of Groundwater Quality in an Area of Blackfoot Disease in Taiwan[J]. Water Res, 2004, 38: 148–158.
[50]吴锋.基于AHP与CBR技术的注射模智能报价方法研究[D].浙江大学硕士学位论文,2008:30-35.
[51] Demigha S. The Retrieval Process in the SAFRS System with the Case-based Reasoning Approach. Iceis 2007: Proceedings of the Ninth International Conference on Enterprise Information Systems, 2007: 468-473--519.
[52]郭劲松,王红,龙腾锐.水环境质量的隶属度BP网络决策模型[J].中国给水排水,2000,16(3):9-11.
[53]龙腾锐,郭劲松,霍国友.水质综合评价的Hopfield网络模型[J].重庆建筑大学学报,2002,24(2):57-60.
[54] Hong S H, et al. Monitoring of Sequencing Batch Reactor for Nitrogen and Phosphorus Removal Using Neural Networks. Biochemical Engineering Journal, 2007. 35(3): 365～370.
[55] Sonil R I,侯德刚,张振家,周伟丽,迟莉娜.基于BP人工神经网络的生化处理水水质预测[J].现代化工,2009,29(12):66-70.
[56] Jia Chun-jiang, Chen Guang-xue, Li Xiao-zhou. Research on the MathematicalModel and Air-Drying Mechanism of Water-based Ink. Printing and Packaging Study, 2011, 174: 31-384.
[57]黄真理,李玉樑等.三峡水库水质预测和环境容量计算[M].北京:中国水利水电出版社,2006.1
[58]赵英.地表水源水质预测模型数据挖掘技术及其适用性研究[D].哈尔滨工业大学博士学位论文,2008.
[59] Franceschini, Samuela T, Christina W. Assessment of Uncertainty Sources in Water Quality Modeling in the Niagara River. Advances in Water Resources, 2010, 33(4):493-503.
[60]王祎.基于不确定性原理的二维随机水质模型及原型系统开发[D].哈尔滨:哈尔滨工业大学硕士学位论文,2009:19-24.
[61] Sprague R H. A Framework for the Development of Decision Support Systems[J]. Management Information Systems, 1980, 4:1-26.
[62] Haidary A, Nakane K. Sensitivity Analysis of Stream Water Quality and Land Cover Linkage Models Using Monte Carlo Method[J]. International Journal of Environmental Research, 2010, 4(1):121-130.
[63] Wang H Q. Improvement of the Recognition Probability about Camouflage Target Based on BP Neural Network[J]. Spectroscopy and Spectral Analysis, 2010, 30(12): 3316-3319.
[64] Sànchez M, Giberta K, Sojdad R S,et al. Chapter Eight Intelligent Environmental Decision Support Systems. Developments in Integrated Environmental Assessment, 2008, 3:119-144.
[65] Haseman W D. GPLAN: an Operational DSS[J]. Data Base, 1977, 8(3):15.
[66]钱程,姚瑶.情景分析法在黑龙江省水环境污染防治工作中的应用[J].环境科学与管理,2006,1:78-80.
[67]易征,李蜀庆,周丹丹.情景分析法在长江上游环境污染治理中的应用[J].环境科学与管理,2009,2:90-93.
[68] Lautenbach S, Berlekamp J, Graf N, et al. Scenario Analysis and ManagementOptions for Sustainable River Basin Management: Application of the Elbe DSS[J]. Environmental Modelling and Software, 2009, 24:26–43.
[69] Yoshimura C, Zhou M. 2020s Scenario Analysis of Nutrient Load in the Mekong River Basin Using a Distributed Hydrological Model[J]. Science of the Total Environment, 2009, 407:5356–5366.
[70] Jessel B, Jacob J. Land Use Scenario Development and Stakeholder Involvement as Tools for Watershed Management within the Havel River Basin[J]. Limnologica, 2005, 35:220–233.
[71] Wolter M H, Guercke. Multi-Agent Based Distributed Power Flow Calculation. Ieee Power and Energy Society General Meeting, 2010:6545.
[72]左春荣.基于定性推理理论和Multi-Agent的群决策支持系统研究[D].合肥:合肥工业大学博士学位论文,2008.
[73]詹伟,王兆红,邱菀华.基于Multi-Agent的分布式项目管理决策支持系统研究[J].计算机应用研究,2007:63-65.
[74]于卫红. RMI在海上搜救智能决策支持系统中的应用[J].计算机工程,2009,12(35):154-158.
[75]陈登科,张大发,江玮等.基于MAS的船用压水堆核事故应急决策支持系统[J].核动力工程,2007,5(28):79-82.
[76] Gonzalez-Velez H M. Health Agents: Distributed Multi-Agent Brain Tumor Diagnosis and Prognosis[J]. Applied Intelligence, 2009, 30(3):191-202.
[77]赵建军.基于多Agent的流域洪水预报系统研究与应用[D].西北农林科技大学硕士学位论文,2008:5.
[78]迟嘉昱.基于网格的开放式决策支持系统研究[D].湖北:华中科技大学硕士学位论文,2004:7.
[79]卿晓霞.基于Multi-Agent的分布式污水处理智能化系统研究[D].重庆:重庆大学博士学位论文,2007:1.
[80]许劲.基于传质原理的二维随机水质模型研究与应用[D].重庆:重庆大学博士学位论文,2007.
[81] Wang Y , Wang P, et al. Two-D Stochastic Water Quality Model Based on Mass Transfer Theory for Warning Emergency Water Pollution Accidents[J]. Progress in Environmental Science and Technology, 2009:1380-1384.
[82]张秋花,薛惠锋,吴介军.多智能体系统MAS及其应用[J].计算机仿真,2007,24(6):133-137.
[83] Shoham. Agent-oriented Programming[J]. Artificial Intelligence, 1993, 60(1):51-92.
[84]郭亮,王鹏,赵英.基于BP神经网络的松花江四方台水质预测[J].哈尔滨工业大学学报,2009,41(6):62-66.
[85]宋国君,谭炳卿等.中国淮河流域水环境保护政策评估[M].北京:中国人民大学出版社,2007年第1版,348-361.
[86] Tao J E. The Game Model Theory and Practice Analysis On Environmental Management. Enivironment Materials and Environment Management, 2010, 13 :1415-1418.
[87]许东,吴铮.基于MATLAB6.x的系统分析与设计——神经网络[M].第二版.西安:西安电子科技大学出版社,2002:19-24.
[88] Haidary A, Nakane K. Sensitivity Analysis of Stream Water Quality and Land Cover Linkage Models Using Monte Carlo Method[J]. International Journal of Environmental Research, 2010, 4(1):121-130.
[89] Miller K D, Waller H G, Scenarios. Real Options and Integrated Risk Management[J]. Long Range Planning, 2003, 36(1):93-107.
[90] Pai P F, Lee F C. A Rough Set Based Model in Water Quality Analysis[J]. Water Resources Management, 2010, 24(11):2405-2418.
[91] Yang S Y, Hu C L. An Ontological Proxy Agent with prediction, CBR and RBR Techniques for Fast Query Processing[J]. Expert Systems with Applications, 2009, 36(5):9358-9370.
[92] Chakravarthy V N. Advanced Medical Image Analysis using GIIM, IISM with CBR and RBR, Intelligence Proceedings of World Academy of Science[J].Engineering and Technology, 2007, 22:553-557.
[93] Jani H M, Peck L S. Applying Machine Learning Using Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) Approaches to Object-Oriented Application Framework Documentation[J]. Third International Conference on Information Technology and Applications, 2005(1): 52-57.
[94]姜继平.水污染危险区域判定及其原型系统开发[D].哈尔滨:哈尔滨工业大学硕士学位论文,2008.
[95] Guo P Y, Shi B Q, Xiao C Y, et al. Calculation Method of Mechanical Structure Complex Time-dependent Reliability with Blind Number Theory and Its Implementation[J]. Engineering and Digital Enterprise Technology, 2009, 16:816-820.
[96]田军,张朋柱,王刊良.基于德尔菲法的专家意见集成模型研究[J].系统工程理论与实践,2004,1:57-62.
[97]李如忠,洪天求,钱家忠.基于盲数的区域水资源可持续利用评价研究[J].中北大学学报(自然科学版),2006,5(27):436-441.
[98]张一喆，樊玉茹，李彩琴.松花江流域污染防治的科研工作[J].北方环境,2003(4):12-15.
[99] Trianni G F, Dell A. Geographic Information System (GIS) Aided Persegment Scene Analysis of Multitemporal Spaceborne Synthetic Aperture Radar (SAR) Series with Application to Urban Areas[J]. International Journal of Remote Sensing, 2010, 31(22): 6005-6014.
[100]Facchinei F, Fischer A, Piccialli V. Generalized Nash Equilibrium Problems and Newton Methods[J]. Mathematical Programming, 2009, 117:163-194.
[101]曾文慧.越界水污染规制——对中国跨行政区流域污染的考察[D].复旦大学博士学位论文,2005:2.
[102]Jeste D V, Ardelt M, Blazer D, et al. Expert Consensus on Characteristics of Wisdom: A Delphi Method Study[J]. Gerontologist, 2010, 50(5):668-680.
[103]Wang S C, Chen M K. A Hybrid Delphi-Bayesian Method to EstablishBusiness Data Integrity Policy A Benchmark Data Center Case Study[J]. Kybernents, 2010, 39(5):800-824.
[104]Hart M D. A Delphi Study to Determine Baseline Informatics Competencies for Nurse Managers[J]. Cin-Computers Informatics Nursing, 2010, 28(6): 364-370.
[105]Garrett Hardin. The Tragedy of the Commons[J]. Science, 1968, 162:1243-1248.
[106]Lu Y Q, He Y T. Research on Game Theory of Motivation and Mechanism of Cooperative Innovation in Multinational Technical Alliance[J]. Proceedings of the 1st International Conference on Sustainable Construction and Risk Management, 2010, 1: 1174-1178.
[107]Muller J P, Farolfi S, Bonte B. An Iterative Construction of Multi-agent Models to Represent Water Supply and Demand Dynamics at the Catchment Level[J]. Environmental Modelling and Software, 2010, 25(10):1130-1148.
[108]Feng X, Cao K, Ma H. Pinch Multi-agent Genetic Algorithm for Optimizing Water-using networks[J]. Computers and Chemical Engineering, 2007, 31(12): 1565-1575.
[109]林高松,李适宇,江峰.河流允许排污量公平分配的多准则决策方法[J].环境科学学报,2007,27(3):494-500.
[110]Manfred Hauswirth. A Component and Communication Model for Push Systems[C]. Proc. of the 7th European Software Engineering Conference, 1999:20-38.