The environmental dimension of national security: A test of systems analysis methods

The systems approach permits us to analyze national security as a cluster of interconnected elements, in which the environmental dimension appears to be the most important one. The environmental problem is divided into two main aspects: environmental security per se and the impact of environment on the overall status of a nation's security. It is argued here that the quality of life and health serve as both the main objective and the principal criterion of environmental security in a social system. Indices of these two factors are used in this article as indicators of the state of this type of security. They confirm that vast areas of Russia, the Ukraine, and Central Asia (especially the Aral Sea region) should be considered as presenting a substantial risk to local people and even producing global impacts on both natural and man-made systems. Environmental factors that destabilize national security are also divided into two groups: those that impact social systems directly and negatively (mainly natural disasters) and technological and sociopolitical agents that cause indirect impacts, in both war and peace time, as well as in the civil and military sectors of the economy. Developments in the former Soviet Union (the Commonwealth of Independent States) are used as an illustration of the consequences that such impacts may have on the status of national security.     

沿海农田林网建设关键技术的经济学择优

对江苏沿海地区农田林网的经营类型、造林树种、林带配置进行了技术经济学分析和多目标灰色局势决策择优。结果表明：农田林网经营类型宜选择复合经营的果材兼用型林网。造林树种可选择速生丰产的杨树等用材树种和经济效益高的桃树等果树。林网配置在海堤河岸上选择用材树种，营造基干林带，筑起大网格框架；在道路沟渠上立体配置林果树种，营造主林带和副林带，构建中网格；在脱盐排水沟上添加果树辅助林带，建立小网格，形成多树种多林种合作、大中小网格配套、多道防线联防、多效益多产品产出的复合配置模式。     

城市水环境安全性与城市可持续发展

城市水环境安全性对城市的可持续发展具有重要意义。目前由于我国城市水环境持续恶化，城市居民的健康受到严重损害，生命和财产安全受到威胁，限制了城市经济和社会发展。本文分析了我国城市水环境现状以及面临的三个主要问题，即水污染严重、地下水超采引发地面塌陷和沉降等环境地质问题、城市防洪安全性差等，提出了相应对策，如加强水污染控制。促进城市污水收集和处理系统建设，采用3S技术(GIS、GPS、RS)进行水质监测，重点做好水源地的水质保护和解决富营养化问题；对地下水实行限量开采和人工回灌；采取节水措施，进行雨水和中水利用；加强城市防洪；开展水环境承载力研究；改革水环境管理体制等。     

应用BP神经网络预测城市需水量的探析

在对辽宁省营口市城市需水量预测中，采用BP人工神经网络方法对该市人均综合用水量进行预测。进而预测出城市需水量，取得了满意效果。从精度检验、数据类比和趋势分析等角度，论证了此种预测方法的可行性和有效性。     

昆明市环境监测信息系统的开发与完善

通过环境监测信息控制系统管理环境监测数据,可提高数据管理水平和数据处理质量、速度,可将环境监测信息尽快地转化为决策依据,有利于提高环境管理和环境污染治理决策水平.本文总结了昆明市环境监测信息系统的设计、开发要点并提出了进一步完善的展望.     

基于神经网络的红树林景观特性遥感提取技术研究

利用神经网络法对广西山口红树林保护区2003年1月8日获取的Landsat TM 卫星图像进行分类.Landsat TM5卫星数据的3、4、5假彩色合成图像空间分辨率比较好,影像层次丰富,地物界限清晰.因此,采用3、4、5波段作为输入层.经过目视判读和现场调查,将输出层单元设置为5个,包括是红树林、陆地植被、裸地(其他建筑用地)、水体和虾池.经过流程实际运算和精度对比,设定训练阈值的贡献是0.9,训练率是0.2,训练次数是1000,最大个体允许误差为0.1.分类精度达到86.86%,高于最大似然分类(50.79%)和光谱角分类精度(75.39%),从而最终获取红树林生态系统的景观格局指标.结果显示研究区红树林均匀度(1.7789)和聚集度指数(0.6854)较高;红树林种类的优势度指数是-1.5850,为弱势群体;红树林的斑块破碎化指数是0.0325,破碎化程度较低.     

资源安全及其基本属性与研究框架

国内外对资源安全问题的关注由来已久,但系统研究此问题还是近年的事情。在此,对资源安全问题的由来、基本属性、研究框架等进行系统论述。认为资源安全是一个国家或地区可以持续、稳定、及时、足量和经济地获取所需自然资源的状态或能力;资源安全具有数量、质量、结构、均衡和价格等方面的含义;并对资源安全按过程、主体和类别等进行了分类;提出应树立开放、动态、持续和系统的资源安全观;资源安全研究应主要包括资源安全概念与属性、资源安全测度指标体系与预警系统、国家资源安全态势、国家资源安全战略等内容。     

模糊神经网络方法在船舶油污事故损害赔偿评估中的应用研究

针对船舶油污事故损害赔偿评估非线性系统的复杂性，分析了人工神经网络技术在该领域应用的可能性，并以20个国际油污基金公约所承认著名赔偿案例为例建立船舶油污事故的神经网络评估模型，该模型对中国船舶油污事故损害赔偿评估具有一定的参考实用价值，为中国船舶油污事故损害赔偿评估提供了一种新颖而方便的计算方法。     

建设先进的环境监测预警体系是国家环境安全的需要

建设先进的环境监测预警体系是中国环境保护发展进程中首次提出的新观点,是做好新形势下的环保工作,加快实现三个转变的需要;是全面反映环境质量状况和趋势,准确预警应急突发环境事件的需要;是现代化环境管理工作的需要;是树立负责任大国国际形象的需要;是国家环境安全的需要.     

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.