化工设备重要度的模糊聚类分析

在层次分析和模糊集理论的基础上，建立了化工设备重要度的模糊层次分析模型，应用判断矩阵分级求出各指标因素对子层次和目标层次的权重。根据模糊数学中的广义加权海明距离，对化工设备重要度进行了模糊聚类分析，得到了符合实际的分类结果。     

模糊聚类分析在区域自然灾害区划的应用：以四川省为例

本文通过对四川省自然灾害分布规律的分析,提出其区划的原则和要求,并引入模糊聚类的方法,对四川省自然灾害进行分区划片,目的是为区域规划提供减灾的重点方向。     

滑坡稳定性的模糊综合评定：以陕西渭南地区重点滑坡为例

本文选用地形地貌、岩性结构、构造、大气降雨、水文地质和人类活动6个定量化的环境因子作为评定滑坡稳定性的标志。利用模糊综合评判方法,对陕西渭南地区数十个滑坡的目前稳定性作了分级判别。按渭南地区的实际情况将其稳定性分为稳定滑坡、较稳定滑坡、不稳定滑坡三级,其结果与实际较为符合。     

模糊综合评判在水质综合评价中的应用

本文应用模糊综合评判对某河段1988年水质现状综合评价,其结果符合实际状况.它既较好地描述了水环境要素连续、多变、复杂的模糊特征,又对污染程度有明析的认识,并客观、正确地刻划和表达了多要素共同作用的结果,故模糊综合评判用于水环境质量综合评价是一种有效和可行的方法。     

城市大气采样点的模糊聚类优选方法

运用数理统计模糊聚类分析方法对各大气采样点获得的监测数据进行聚类相关分析,优选出较科学、合理且足以能反映城市大气污染水平、分布特征及时空变化规律的采样点。文中详细介绍了优选方法。     

An Assessment of Ecosystem Risks in the St. Croix National Scenic Riverway

The information needed for conducting the risk assessment was provided by the participants in a 2-day workshop. The invited participants, who possessed knowledge of the St. Croix ecosystem, identified through a group-consensus process a list of stressors and a list of ecosystem values. They then assigned numerical values to each stressor-ecosystem value pair that reflected the degree to which the given stressor contributes to ecosystem risk as measured by the given ecosystem value. Based on this information, the analytical portion of the methodology was then used to rank the ecosystem risks (stressors) when examined from several different perspectives: immediate impact, time-duration, and management activities. Regardless of the perspective taken, riverway development emerged as the most significant stressor.     

山东省城市缺水模糊评价及对策

分析了山东省水资源特点,利用模糊数学中的多因子综合评判方法,采用Windows XP平台下Visual Basic6.0建立了水资源紧缺程度评价系统,综合评价了山东省17个地级城市的水资源紧缺程度及其原因,并提出了解决对策。结果表明17个地级城市都存在不同程度的缺水现象,而水污染是导致17个城市水资源短缺的主要因素之一。2000年到2005年属于严重缺水的城市数量由6个增加到12个,水资源短缺的形势呈逐年加剧趋势。     

A fuzzy expert system for soil characterization

As soil is a natural resource not always renewable, the risk characterization of contaminated soils is an issue of great interest. Artificial Intelligence (AI), based on Decision Support Systems (DSSs), has been developed for a wide range of applications in contaminated soil management. Decision trees have already shown to be easy to interpret and able to treat large scale applications. Fuzzy logic gives an improvement in the perturbations and the variance of the training data, due to the elasticity of fuzzy set formalism. In this study, we have developed a classificatory tool applied to characterize contaminated soil in function of human and environmental risks. Knowledge engineering for constructing the Soil Risk Characterization Decision Support System (SRC-DSS) involves three stages: knowledge acquisition, conceptual design and system implementation. A total of 26 parameters were divided into three groups to facilitate the configuration of the expert system: source attributes, transfer vector attributes, and local properties. Sixteen case studies were evaluated with the SRC-DSS. In comparison with other techniques, the results of the current study have shown that SRC-DDS is an excellent tool to classify and characterize soils according to the associated risk.     

Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)?

The present study aims to clarify the necessity and effectiveness of considering fuzziness in modelling fish habitat preference, and the advantages which would be achieved by considering it. For this purpose, genetic algorithm (GA) optimized habitat preference models under three different levels of fuzzification were compared with regard to prediction ability of the habitat use of Japanese medaka (Oryzias latipes) dwelling in agricultural canals in Japan. Field surveys were conducted in agricultural canals in Japan to establish a relationship between fish habitat preference and physical environments of water depth, current velocity, lateral cover ratio and percent vegetation coverage. The habitat preference models employed for testing the fuzzy-based approach were category model, fuzzy habitat preference model, and fuzzy habitat preference model with fuzzy inputs. All the models were developed at 50 different initial conditions. The effectiveness of the fuzzification in fish habitat modelling was assessed by comparing mean square error and standard deviation of the models, and fluctuation in habitat preference curves evaluated by each model. As a result, the effect of fuzzification appeared as smoother curves and was found to reduce fluctuation in habitat preference curves in proportion to the level of fuzzification. The smooth curves would be appropriate for expressing uncertainty in habitat preference of the fish, by which fuzzy habitat preference model with fuzzy input achieve the best prediction ability among the models. In conclusion, the present study revealed that there are two advantages of fuzzification: reducing fluctuations in habitat preference evaluation and improving prediction ability of the model. Therefore, the consideration of fuzziness would be appropriate for representing fish habitat preference under natural conditions.