环境预测中的GAM模型

当原始序列属代数曲线型时，应用ＧＡＭ模型可以提高精度，从而避免进行ＧＭ（１，１）模型的残差修正二次建模。从分析ＧＭ（１，１）模型的缺陷着手，结合实例详细介绍了ＧＡＭ模型的思想形成和计算方法。     

也谈GAM与GM（1,1）模型

通过对环境预测中灰色建模方法的回顾，说明了ＧＡＭ和ＧＭ（１，１）模型各有所长，最重要的是建模手段的丰富。     

纺织厂车间噪声的灰色GIM（1）预测模型

利用灰色ＧＩＭ（１）模型的非时序直接建模法原理,对纺织厂织造车间的环境噪声分别建立了等间隔和不等间隔的非时序预测模型。实测研究表明ＧＩＭ（１）模型优于常规ＧＭ（１,１）模型,且所需建模数据大为减少,预测精度更高（达９９．９９％）,从而拓宽了灰色预测的适用范围。     

GEMS／Air介绍（1）1993年监测报告（I）

ＧＥＭＳ／Ａｉｒ介绍（１）１９９３年监测报告（Ｉ）朱玉栋（中国环境监测总站，北京１０００１２）全球环境大气监测（简称为ＧＥＭＳ／Ａｉｒ）１９９３年在中国的工作情况如下；１．ＧＥＭＳ／Ａｉｒ监测站位情况目前在中国参加ＧＥＭＳ／Ａｉｒ工作的城市共有五个，...     

带有阶跃函数的GIM（1）预测模型及其应用

结合某水域地面水ＲＯＤ５的预测实践,研究提出了带有阶跃函数的ＧＩＭ（１）预测模型,并给出了估计这种模型参数的简便方法,从而扩大了ＧＩＭ（１）模型在环境系统中的应用范围,实例表明,运用该方法得到的模型比常规ＧＩＭ（１）具有更高的精度。     

地表水COD浓度灰色预测的GPPM（1）模型

根据地表水中ＣＯＤ浓度的时序数据，建立了ＧＰＰＭ（１）预测模型，结果表明ＧＰＰＭ（１）模型的预测精度优于常规灰色ＧＭ（１，１）模型，它为环境系统的拟合，预测和决策提供了新的方法途径。     

GPSM(1)灰色模型在大气降水酸度预测中的应用

介绍了ＧＰＳＭ（１）灰色模型的建模思想，并将其成功地应用于某地大气降水酸度预测，其结果与实际情况一致性很好，表明ＧＰＳＭ（１）模型预测大气降水酸度是可行的     

改进环境影响评价制度监督管理方法的几点考虑

改进环境影响评价制度监督管理方法的几点考虑罗崇富ＳＵＧＧＥＳＴＩＯＮＯＦＩＭＰＲＯＶＩＮＧＳＵＰＥＲＶＩＳＩＯＮＡＮＤＭＡＮＡＧＥＭＥＮＴＯＦＥＩＡＳＹＳＴＥＭ￥ＬｕｏＣｈｏｎｇｆｕ（ＸｉｎｊｉａｎｇＥｎｖｉｒｏｎｍｅｎｔＭｏｎｉｔｏｒｉｎｇＣｅｎｔ...     

GEMS／Water介绍（1）

ＧＥＭＳ／Ｗａｔｅｒ介绍（１）朱玉栋（中国环境监测总站，北京１０００１２）ＧＬｏｂａＬＥｎｖｉｒｏｎｍｅｎｔａｌＭｏｎｉｔｏｒｉｎｇＳｙｓｔｅｍ（简称为ＧＥＭＳ）是全球环境监测系统；ＧＥＭＳ／Ｗａｔｅｒ是全球环境监测系统中的水质监测，通常称为全球水质...     

用工业废水排放量预测地面水CODMn灰色方法研究

ＧＰＭ（１）灰色生长曲线常被用于等距时序环境系统的分析建模、非等距摆动空间序列方面的应用尚未报道。本文研究提出了将非等距摆动空间序列经过等距化处理的ＧＰＭ（１）线性回归优化建模方法采用该方法所建的非等距ＧＰＭ（１）模型用于工业废水量预测地面水ＣＯＤＭｎ的实例表明，该方法简捷，方便、精度高于回归分析，有较大实用价值。     

The Geocybernetic Assessment Matrix (GAM) — A new assessment tool for evaluating the level and nature of sustainability or unsustainability

Evaluating sustainability from EIA-based assessments has been problematic at best. This is due to the use of reductionist and qualitative approaches which is dependent upon the perspective of the assessor(s). Therefore, a more rigorous and holistic approach is required to evaluate sustainability in a more consistent way. In this paper, a matrix-based methodology in order to assess the indicated level and nature of sustainability for any project, policy, indicators, legislation, regulation, or other framework is described. The Geocybernetic Assessment Matrix (GAM) is designed to evaluate the level and nature of sustainability or unsustainability occurring in respect the fundamental and complex geocybernetic paradigms. The GAM method is described in detail in respect to the theory behind it and the methodology. The GAM is then demonstrated using an appropriate case study — Part 1 of the UK Climate Change Act (2008) concerning carbon budgets and targets. The results indicate that the Part 1 of Act may not achieve the desired goals in contributing towards sustainable development through the stated mechanisms for carbon budgets and targets. The paper then discusses the broader context of the GAM with respect to the core themes evident in the development and application of the GAM of: sustainability science; sustainability assessment; application value of the GAM; and future research and development.     

环境振动的灰色预测模型

利用GIM(1)的非时序直接建模法预测研究建筑施工的环境振动 ,并将GIM(1)模型与GM (1,1)模型进行比较分析 ,结果表明GIM(1)模型的拟合精度优良 ,对原始资料中白化信息的利用更加丰富 ,拓宽了GIM (1)模型在环境科学领域中的应用范围     

灰色系统模型在高锰酸盐指数预测中的应用

以某地地表水中高锰酸盐指数年平均浓度变化为依据 ,应用灰色系统中 GM(1 ,1 )模型 ,对高锰酸盐指数浓度进行预测分析。     

灰色系统模型GM（1,1）在大气环境浓度预测中的应用

运用灰色系统理论，结合实例详细介绍了ＧＭ（１，１）模型在大气环境浓度预测中的应用。     

GM（1,1）模型简化算法及其在噪声影响预测中的应用

对ＧＭ（１，１）模型经差分形式化及令定写成直线方程形式。据最小二乘法原理，原方程的参数辨识可借助线性回归方法来实现。从而使ＧＭ（１，１）模型的应用更显普及和实用化。     

灰色系统模型GM（1,1）的参数估计方法

提出了灰色系统模型ＧＭ（１,１）参数优化估计的一种新方法－－加速遗传算法,分析了该算法控制参数的优化特性,总结了这些参数的简便设置技术,并给出了它的几个应用实例,该方法也可用于其它环境工程优化计算中。     

Modeling distribution changes of vegetation in China under future climate change

Climatic change will result in great changes in vegetation. In this paper, a biogeographical model, the BIOME1, was used to predict potential vegetation distribution in China under climate change. Firstly, the BIOME1 was validated according to the climate–vegetation relationships in China. Kappa statistics showed that the validated BIOME1 was able to capture the geographical patterns of vegetation more accurately. Then, the validated BIOME1 was used to predict the distribution of vegetation of China under two climatic scenarios produced by a Regional Circulation Model, RegCM2/CN. The simulation results showed obvious northward shifts of the boreal, temperate deciduous and evergreen and tropical forests, a large expansion of tropical dry forest/savanna and reduction of tundra on the Tibetan Plateau. Three vulnerable regions sensitive to climate changes are pointed out, i.e., Northern China, the Tibetan Plateau and Southwestern China (mainly Hengduan Mountains in Yunnan Province and west of Sichuan Province). In recent decades, China has experienced dramatic industrialization and population growth, which exert strong pressure on the environment of China. The consequences of climate changes warrant more attention for maintaining a sustainable environment for China.