水环境质量评价的投影寻踪模型及其应用

将投影寻踪分类模型应用于水环境质量综合评价中,利用基于实码的加速遗传算法来优化投影方向,将多维数据指标转换成低维子空间,通过寻求最优投影方向来计算投影函数值,从而根据投影值的大小进行水环境质量评价。结合实例进行了应用,结果较令人满意,为水环境质量评价提供了一条新的方法和途径。     

煤炭产业国际竞争力的遗传—投影寻踪评价

根据煤炭产业发展的实际状况,提出了煤炭产业竞争力评价的指标体系。采用探索性数据分析—投影寻踪法,结合遗传算法,建立了遗传—投影寻踪综合评价模型。以全球11个主要产煤国为对象进行了实证研究,验证了该方法的科学性和实践的可行性,提出提升我国煤炭产业竞争力的对策建议。     

基于集对原理的天然草地分类方法

本文首次将集对原理用于天然草地分类中，以中国新疆某地区的31个天然草地为例进行计算，并将分析结果，与灰色关联度分析法、投影寻踪法以及投影寻踪动态聚类法作了对比分析。结果表明，集对法与三种方法分析结果一致，但集对法计算简单，使用方便，不受样本容量限制，且能清晰和形象地显示所属类别的程度，在多因素聚类分析中具有广阔的应用前景。     

基于投影寻踪动态聚类模型的边坡稳定性评价

投影寻踪聚类(PPC)模型在多因素聚类分析中被广泛应用并取得了满意的效果,然而,该模型还存在诸如密度窗宽参数取值经验确定等不足,有待改进提高。本文针对PPC模型存在的问题,把投影寻踪聚类的思想和动态聚类方法结合起来构造投影指标,建立了投影寻踪动态聚类(PPDC)新模型,新模型在整个运算过程中毋需人为给定参数,聚类结果客观、明确。边坡稳定性评价的实际应用表明,PPDC模型不仅切实可行,取得了理想的效果,而且还具有稳定性好、操作简便等特点。PPDC模型为多因素聚类分析提供了一种新方法,具有广阔的应用前景。     

大气环境质量分析与评价研究的新进展

简要地介绍了人工神经网络（ANN）、物元可拓集（MEES）、集对分析（SPA）、遗传算法（GA）和投影寻踪技术（PP）等的基本思想和特点，评述了近年来国内外将这些新理论和新技术应用于大气环境质量分析与评价取得的最新成果，指出了大气环境质量分析与评价研究的可能发展前景。     

中国九大古都旅游竞争力空间结构分析

以中国九大古都为研究对象,运用层次分析法、投影寻踪模型和主成分分析法,通过投影值-AHP-PP评价指数和主成分分析计算,分析了中国九大古都旅游综合实力和空间格局分布状况。     

低碳经济视角下的城市圈土地节约集约利用评价研究——基于遗传算法的投影寻踪模型

从低碳经济视角出发,运用基于遗传算法的投影寻踪模型对武汉城市圈低碳土地节约基于利用水平进行了评价研究.结果显示,各市低碳土地节约集约利用水平差异较大,发展极不协调,难以有效地实现城市圈协同发展.武汉城市圈应以整体效益最大化为目标,注重保护环境与经济增长相结合,大力发展低碳经济,以协同发展促进低碳土地利用.     

基于土地适宜性评价的区域碳排放权分配研究

资源环境与经济发展的矛盾日益突出,严格控制碳排放是增强可持续性发展的根本途径.研究参考已有文献,将碳平衡引入土地适宜性评价研究,分别从经济、生态、社会角度建立了基于碳平衡的城市圈土地适宜性评价指标体系,具体包括碳源适宜性评价和碳汇适宜性评价.运用基于蚁群算法的投影寻踪模型得到两类适宜性综合评价值.碳平衡适宜性评价结果显示,各城市适宜性差异显著,存在较大的区域内协调潜力.运用多目标规划模型在适宜性评价的基础上对城市圈的碳排放权进行优化配置.碳排放权配置研究可促进低碳经济发展,是生态文明建设的目标之一,对城市圈可持续发展具有重要的指导意义.     

模糊神经网络模型在拟建项目环境质量评价中的应用

由于评价因子与环境质量标准之间的模糊关系,导致传统的评价模型结果的不确定性。本文将模糊理论和神经网络模型相结合,利用模糊理论的隶属度反映各因子的质量相对状态,从而更精确地识别模糊性。通过模糊神经网络模型对环境质量标准样本的学习和测试,得出评价等级,并与模糊综合评价方法进行对比,实验结果表明,模糊神经网络模型用于环境质量评价是可行的,且比模糊综合评价方法更为客观、合理。     

神经网络方法在地表高程预测中的应用研究

从地表高程预测的研究现状出发,基于Matlab神经网络工具箱构建了广义回归神经网络(GRNN),建立了能够预测地表高程的神经网络预测模型,并与反距离加权法(IDW)的预测结果进行了比较分析。结果表明,GRNN方法计算简单,在相同的样本数据条件下,GRNN的预测精度较高,表明神经网络方法在地表高程预测中的应用是可行的。     

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.     

基于ANN的环境质量评价

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理，具有自学习和自适应等一系列优点，因而用它来评价环境质量是可行的。本文针对环境质量评价问题，建立了基于神经网络的评价系统，给出了应用实例。     

几种神经网络模型在空气质量评价中的应用

人工神经网络是一种利用计算机模拟人脑神经组织的运算模型。作为一种新的研究方法。神经网络在一定程度上可以弥补传统评价方法需要构建隶属函数、无法精确描述级别区间内的变化特征以及设计过程具有一定人为偏好的不足。本文选用BP网络、径向基函数网络、LVQ网络和Elman网络这4种典型的神经网络模型进行实例研究。把国内8个城市的污染物排放数据代入训练好的网络模型，进行空气质量评价，得出的结论对提高神经网络评价结果的准确性和可靠性有一定参考作用。     

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP,artificial neural network: A case study

The increasing growth of the economy in each country necessitates a great amount of investment in infrastructure. The belief that projects involve various uncertainties, such as technical skills, management quality, and the like, indicates that most projects fail to achieve their aims, interests, costs, as well as their timeframes and space requirements. As the environment can pose significant uncertainty to any project, environmental risks should be deeply studied by project management departments. This study intends to analyze as a case the environmental risk management system within a consulting firm. From this analysis, each aspect of a project's environmental risk management is ranked using a fuzzy analytical network process (ANP), a neural network algorithm, and a decision‐making trial and evaluation laboratory (DEMATEL) methodology. From the organizational aspect, budget risk is the most significant. From the technical aspect, the risk of regulations is the most important one. Finally, the risk of project failure from poor communication is another identified main risk in this research. By studying high‐ranking items in this hierarchy, it can be understood that these criteria exist in different aspects; therefore, all aspects of the risk should be taken into account to cover and assess risk. A neural network algorithm for validating and reassessment of ranking is employed. Results of this application showed that, based on Spearman's rank correlation method, two different approaches resulted in similar rankings. Finally, some practical implications for responding to the most highly ranked risks are proposed.     

基于BP神经网络的城市旅游环境质量评价

城市旅游环境质量与城市旅游可持续发展之间存在着极为密切的关系,实现可持续发展的关键就是要解决好城市旅游环境质量问题。而旅游资源环境、旅游自然生态环境、旅游社会服务环境、旅游信息环境是城市旅游环境质量评价的主要因素,利用BP神经网络的方法对城市旅游环境质量进行评价是十分科学有效的。     

An online water quality monitoring and management system developed for the Liming River basin in Daqing, China

This paper describes an online water quality monitoring and management system that was developed by combining a chemical oxygen demand sensor with an artificial neural network technology and a virtual instrument technique. The system was used to model the hydrological environment of the Liming River basin in Daqing City, China, in an effort to maintain the water quality in this basin at a level compatible with the status of Daqing City as a scenic resort. Operation of the system during the past 2 years has shown that an optimal allocation of water (including water released from an environmental reservoir to mitigate pollution events) could be achieved for the basin using the information gathered by the system; using mathematic models established for this system, the quantity of water released from the reservoir is adequate to improve the overall water environment. The results demonstrate that the system provides an effective approach to water quality control for environmental protection.