神农架国家级自然保护区樟科资源植物分析

神农架樟科植物种类共有 7属 3 1种 (含变种 ) ,资源植物十分丰富 ,按其用途可分为观赏植物类、蜜源植物类、药用植物类、木材植物类、香料植物类、工业用油脂植物类及珍稀保护植物类等。这些资源植物具有较大的开发利用价值。     

CPI对我国旅游上市公司盈利能力的影响研究

基于2001-2010年CPI与15家旅游上市公司的财务数据,通过最小二乘回归模型和假设检验,分析了CPI变化对我国旅游上市公司盈利能力的影响,比较了CPI对景区类、酒店类、综合类旅游上市公司盈利能力影响的大小.结果发现,CPI对我国旅游上市公司盈利能力的影响是反向的和显著的；CPI对景区类、酒店类、综合类旅游上市公司盈利能力的影响存在差异；综合类高于景区类和酒店类,景区类高于酒店类.根据上述情况分析原因,提出相关建议.     

灰色聚类分析在环境质量评价中的应用

环境是一个“灰色系统”，可以将灰色聚类分析应用到大气环境评价中。灰色聚类可按６个步骤进行：①给出聚类白化值；②确定灰类的白化函数；③求标定聚类权；④求聚类系数；⑤构造聚类行向量；⑥聚类。将灰色聚类分析方法应用于某油田８个主要矿区的ＳＯ２、ＴＳＰ和ＮＯｘ污染物，得出它们的大气环境质量的相应等级。     

浅谈工业类项目施工期环境监理的重点

文章结合工业类项目与生态类项目的区别,以及青海省工业类项目环境监理的实践,重点阐述工业类项目环境监理的重点问题,即批建符合性环境监理、污染防治设施与措施环境监理和风险防范设施与措施环境监理。为工业类项目施工期环境监理的开展提供一定的借鉴。     

大围山区野生蔬菜资源及其开发利用

对大围山区野生蔬菜资源调查结果表明:区内现有野生蔬菜隶属于叶菜类、茎芽类、块根类、花菜类、果类、种子类、食用菌类、竹笋类、葱蒜类9大类,共计220余种.阐述了大围山区野生蔬菜资源开发利用现状,提出了开发与保护对策.     

光致抗蚀剂乳化废水中有机物GC/MS分析

采用色谱－质谱（GC／MS）联用分析仪分析了电子工业光致抗蚀剂乳化废水中的有机化合物成份，测定了该废水中各种有机污染物的含量。分析结果表明：电子工业中光致抗蚀剂乳化废水的主要成份为有机酸类、醛类、酯类、胺类、芳香酮类、酸酐类、砜类、聚醚类、醇类、芳香烃类、杂环类十一大类有机化合物，其中聚丙二醇类、脂肪酸类、丁烯酸类、苯甲酸类、苯甲酸胺、烷基酚聚氧乙烯醚类和噻吩类约占总有机碳含量的95％以上。有机污染物的分析测定对此类废水的处理工艺的研究具有重要的指导作用。     

基于主成分分析与层次聚类分析的水质综合评价

为明确多指标评价体系下泸沽湖水质综合现状,达到分区重点治理的目的。调查共采集泸沽湖28个点位水样。采用主成分分析法和层次聚类分析法对8个水质参数进行分析,并将6种基于类间距的层次聚类结果与综合营养状态指数法评价结果进行比较。结果表明:亮海大部分区域为Ⅱ类水质,而草海沿线水质逐步恶化至V类。采用欧式距离下最长距离法可获得与实际相符的最优聚类结果,该结果表明各点位水质依据不同污染程度可依次聚为5类。分区治理应重点关注类5和类4所代表的中度和轻度富营养化水域。本实验采用的分析方法可直观反映出各点位污染程度与相互关系,对多指标参与计算情况下的水质综合评价有很好的参考价值。     

竹类植物的生态功能

中国是世界上竹类资源最丰富的国家.竹类植物分布广、生长快、产量高、用途广,一年种植可连年持续利用,具有极高的生态价值,其特殊的生物学特性对环境改善、水土保持具有不可忽视的重要作用.根据前人的研究成果,对竹类植物的生态功能作用进行了分类描述,以期对竹类植物的生态功能开发提供参考,进一步推动竹类植物的生态研究.     

环境类高技能人才培养研究

环境类高技能人才是我国环境污染防治工作的核心骨干,是推动科技成果转化不可或缺的重要力量。然而环境类人才就业不乐观,缺乏专业的高技能培训体制,政府投入不够,企业培训力度不够,环境类高技能人才培养慢、晋升难等,造成了我国环境类高技能人才资源匮乏、分布不均。这些问题可以通过加大政府投入,完善高职教育评价体系,充分发挥企业的主体作用和提高环境类高技能人才待遇的方法得以解决。     

塑封基板类器件翻新件无损鉴别方法研究

本研究提出了一种针对塑封基板类翻新件的无损鉴别方法。通过外观特征分析、声学扫描电子显微镜分析和X射线分析等技术方法,实现了塑封基板类翻新件的无损鉴别方法。结合相关案例,揭示了塑封基板类器件的典型翻新物理特征,有效指导了塑封基板类翻新器件的鉴别方向。针对典型翻新物理特征进行综合评价和风险分级,进一步加强了塑封基板类器件在应用过程中的风险规避。     

Chemometrical exploration of the wet precipitation chemistry from the Austrian Monitoring Network (1988-1999)

The present paper deals with the application of different chemometric methods to an environmental data set derived from the monitoring of wet precipitation in Austria (1988-1999). These methods are: principal component analysis (PCA); projection pursuit (PP); density-based spatial clustering of application with noise (DBSCAN); ordering points to identify the clustering structures (OPTICS); self-organizing maps (SOM), also called the Kohonen network; and the neural gas (NG) network. The aim of the study is to introduce some new approaches into environmetrics and to compare their usefulness with already existing techniques for the classification and interpretation of environmental data. The density-based approaches give information about the occurrence of natural clusters in the studied data set, which, however, do not occur in the case presented here; information about high-density zones (very similar samples) and extreme samples is also obtained. The partitioning techniques (clustering, but also neural gas and Kohonen networks) offer an opportunity to classify the objects of interest into several defined groups, the patterns of ionic concentration of which can be studied in detail. The visual aids, such as the color map and the Kohonen map, for each site are very helpful in understanding the relationships between samples and between samples and variables. All methods, and in particular projection pursuit, give information about samples with extreme characteristics.     

一种改进的模糊聚类方法在大气环境质量评价中的应用

一般的模糊聚类分析方法只能解决大气污染状况的顺序问题,或只能得出此地与彼地的大气质量状况的相似程度,而不能同时确定大气质量的具体等级。本文提出的一种改进的模糊聚类分析方法解决了这一问题、该方法的主要改进在于:①以各污染因子的污染程度的分级标准值为聚类中心,待分样本与聚类中心之间的相似系数或待分样本隶属于某一类的隶属函数作为聚类函数;②相似系数设计为几何平均最小型,隶属函数设计为正态分布型。实例计算和比较表明,该方法是大气环境质量评价中的一种较好的方法。     

化工区风险源的灰色聚类分级方法的研究

建设化工区需对企业进行合理规划布局,且化工区风险预警和防范具有优先性问题,需要对企业进行有效的风险分级。现有研究中的区域风险源分级方法上存在较大的不足。根据化工区风险源的特点,基于环境风险评价和灰色系统的基本理论,探索性的将灰色聚类分析方法应用于化工区风险源分级中。建立了一套适合化工园区的指标,对各个企业进行聚类分析,划分风险等级。以大连市某化工区为例,筛选8个指标,并选取了6个化工企业进行灰色聚类分析。将它们归为4个不同的风险级别。该方法不仅能较全面地反映化工区各个风险源的风险级别,对风险源的规划、布局和管理起到较好的决策支持作用,而且能指导预警和防范体系的建立,合理分配有限的资源。     

Strong Influence of Variable Treatment on the Performance of Numerically Defined Ecological Regions

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale “sub-domains” defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.     

Representative Landscapes in the Forested Area of Canada

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada’s land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative—or “exemplar”—from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada’s ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada’s forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach.     

灰色聚类法在水环境质量评价中的应用

用灰色聚类法对乐山市境内的岷江、大渡河、体泉河的水环境质量进行评价，并与水质距离评价法、综合污染指数法进行比较。结果表明，灰色聚类法也是一种对水环境质量评价的实用方法。     

STRATIFICATION OF VARIABILITY IN RUNOFF AND SEDIMENT YIELD BASED ON VEGETATION CHARACTERISTICS1

ABSTRACT: Runoff and sediment yield were collected from 100 plots during simulated rainfalls (100 mm/hr for 15 minutes) at antecedent soil moisture conditions. A clustering technique was used to stratify the variability of a single data set within a sagebrush‐grass community into four groups based on vegetation life form and amount of cover. The four cluster groups were grass, grass/shrub, shrub, and forb/grass and were found to be significantly different in plant height, surface roughness, soil bulk density, and soil organic matter. Stepwise multiple regression analyses were performed on the single data set and each cluster group. Results for individual groups resulted in more robust predictive equations for runoff (r²= 0.65–0.73) and sediment yield (r²= 0.37–0.91) than for equations developed from the single data set (r²= 0.56 for runoff and r²= 0.27 for sediment yield). The standard errors of the cluster group regression equations were also improved in three of the four group equations for both runoff and sediment yield compared to the single data set. Runoff was found to be significantly less (p >0.01) in the forb/grass group compared with other vegetation cluster groups, but this was influenced by four plots that produced little or no runoff. Sediment yield was not found to be significantly different among any cluster groups. Discriminant analysis was then used to identify important variables and develop a model to classify plots into one of the four cluster groups. The discriminant model could be incorporated into rangeland hydrology and erosion models. The percentage cover of grasses, shrubs, litter, and bare ground effectively stratified about 12 percent of the variation observed in runoff and 26 percent of the variability for sediment yield as determined by r².     

Analysis and Predictive Models of Single‐Family Customer Response to Water Curtailments During Drought1

Polebitski, Austin S. and Richard N. Palmer, 2012. Analysis and Predictive Models of Single‐Family Customer Response to Water Curtailments During Drought. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2012.00691.x Abstract: This research investigates customer response to demand management strategies during two drought periods in the City of Seattle. An analysis of customer response to voluntary water curtailments is conducted using k‐means clustering to identify like groups of customers and behavior patterns. The clustering method identified important variables (household income, lot size, living space, and family size) useful in determining customer response to water curtailments. Ordinary least squares and spatial lag regression models are estimated using the first and second principal components of variables identified in the clustering analysis. Larger values of income, lot size, and living space enhanced water reductions whereas larger family size tended to reduce the effectiveness of curtailments. Projections of changes in Seattle’s built environment and demographics between 2000 and 2030 were obtained from an urban simulation model (UrbanSim) and were processed through the regression models to investigate changes in future curtailment effectiveness. This research found that increasing household size hardened demands (decreased curtailment effectiveness) whereas decreasing household size increased per‐capita curtailment effectiveness. These results suggest that changes in the number of residents within a home is likely to be the most important factor in determining future curtailment effectiveness.     

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.     

Supervised clustering for automated document classification and prioritization: a case study using toxicological abstracts

Machine learning and natural language processing algorithms are currently widely used to retrieve relevant documents in a variety of contexts, including literature review and systematic review. Supervised machine learning algorithms perform well in terms of retrieval metrics such as recall and precision, but require the use of a sizeable training dataset, which is typically expensive to develop. Unsupervised machine learning algorithms do not require a training dataset and may perform well in terms of recall, but are typically lower in precision, and do not offer a transparent means for decision-makers to justify selection choices. In this paper, we illustrate the use of a hybrid document classification method based on semi-supervised learning that we refer to as “supervised clustering.” We show that supervised clustering combines the ease of use of unsupervised algorithms with the retrieval efficiency and transparency of supervised algorithms. We demonstrate through simulations the high performance and unbiased predictions of supervised clustering when provided even with only minimal training data. We further propose the use of ensemble learning as a means to maximize retrieval efficiency and to prioritize the review of those documents that are not eliminated by the supervised clustering algorithm.