区域易损性的模糊综合评判

提出用区域易损度来估计区域易损性，区域易损度所涉及的因素很多，而它的界线又是不分明的，利用模糊综合评判来对其进行研究是一条有效的途径，并且计算方法也简单易行。通过模糊综合评判，可以全方位地了解各地区的易损程度。     

基于GIS栅格数据的空间模糊综合评判方法与实践

针对模糊综合评判多集中于对区域整体的综合评判的局限,提出了基于GIS的空间模糊综合评判方法。介绍了该方法的关键内容——隶属层的形成过程以及基于栅格数据的综合运算方法,并将该方法应用于门头沟煤矿区生态环境综合评价。     

区域易损性的模糊综合评判

提出用区域易损度来估计区域易损性。区域易损度所涉及的因素很多 ,而它的界线又是不分明的 ,利用模糊综合评判来对其进行研究是一条有效的途径 ,并且计算方法也简单易行。通过模糊综合评判 ,可以全方位地了解各地区的易损程度。     

模糊综合评判在大田县均溪河水质评价中的应用

作为福建省大田县最主要的河流之一，均溪河的水质状况对该地区生态环境产生重大影响，因此对其进行客观的评价也具有重要的意义，模糊数学方法克服了过去一些传统方法的主观性较大的缺陷，改进了评价效果。先分析了模糊综合评判方法的原理和评判步骤，然后根据1999年和2001年监测的数据，应用该方法对大田县均溪河的水质进行综合评价，结果表明除个别因素外，其它主要的污染物浓度都有所降低，均溪河水体质量由Ⅲ级上升到Ⅱ级，说明其总体水质状况得到了改善。     

旅游地貌资源的综合模糊评价

旅游地貌资源是一种重要的旅游自然资源。影响旅游地貌资源的因子主要有岩石、构造、侵蚀速度、资源组合、环境容量、知名度、愉悦感等。根据诸因子的相对模糊等级，采用模糊聚类，可以较方便地对旅游地貌资源进行综合等级评价。     

流域水资源丰富度评价的模糊综合评判

针对流域水资源丰富度各等级之间的模糊性，在考虑水资源各影响要素权重大小的基础上，运用模糊综合评判方法建立流域水资源丰富程度评价模型。并将该方法用于辽西沿海诸河流域水资源的丰富度评价。计算方法简便，结论直观，且该文［２］的评价结果更加符合客观实际。     

饭店员工业绩的多因素层次模糊综合评判

业绩考核是一种员工评估制度,它通过系统的方法来测量和评定员工的工作行为和工作效果。以饭店为例,运用多因素层次模糊综合评判法对其员工业绩进行评价,在系统分析基础上建立综合评价模型,经一致性检验后计算各因子的相对权值并模糊打分。结合权值结果和得分,分析了员工在财务、创新、内部业务方面的业绩。评价结果表明,饭店员工在内部业务方面的业绩最好,在财务方面的业绩最差。根据评判结果,可以发现员工工作的长处、短处,以扬长避短,并采取相应地奖惩措施,强化激励与约束机制,能调动全体员工的工作积极性,整个饭店的竞争力和经济效益也随之增强。     

金龙山斜坡中多变形带的稳定性模糊综合评判

斜坡中发育的多种裂面在滑坡形成过程中将组合成多条变形带，随着变形的发展，变形逐渐向最大剪应力处集中，最终形成滑面。斜坡中多变形带稳定性模糊综合评判是采用数理统计的方法，将滑坡发育过程中影响变形带的因素作为不同的评判因子，对其进行模糊综合评判分析，然后确定最不稳定的变形带。文中最后以金龙山滑坡变形带的稳定性分析为例，证明该方法的正确性。     

云南昭通滑坡泥石流危险度模糊综合评判

探讨了云南昭通的滑坡、泥石流危险度模糊综合评判法，介绍了以概率代替隶属频率，并构成单因素模糊矩阵Ｒ．以此为基础，完成了昭通滑坡、泥石流危险度分区．     

Rs和Gis支持下的自然生态环境评价——以南水北调雅砻江工程区为例

利用TM影像、DEM、温度和降水等气象监测数据,提取了影响自然生态环境的5项重要指标:植被指数,海拔高度,土地覆盖,年均温和降水量。根据这5项环境指标,建立综合自然生态指数模型,并利用该模型对研究区进行环境评价,将研究区的自然环境划分了3级。结果表明该方法能有效地适用于研究区自然生态环境的评价。     

综合国力主成分分析与模糊综合组合评价模型

基于模糊理论、统计分析等数学理论,提出一种定量的综合国力模糊综合评价模型.首先对基本数据进行标准化处理和主成分分析,并建立综合国力评判集;其次,根据所选取的主成分及其贡献值建立评判对象因素集,并计算综合国力评判因素权向量;最后,利用综合国力评判集、评判因素权向量及评判对象因素集进行模糊综合评判,根据模糊综合评判模型对13个主要国家的综合国力进行了比较分析.     

模糊层次分析法在山区大农业发展决策中的应用

采用模糊层次分析方法,综合考虑自然资源条件和市场需求,分析并提出陇南山区白龙江流域各垂直气候层中农业各部门的优先发展顺序,为山区大农业发展提供决策依据。     

基于GIS的山地城镇空间拓展土地适宜性评价——以大埔县为例

本文综合运用GIS和RS技术,针对大埔县山地城镇的特点,选取地形地貌因子、生态敏感性因子、限制性保护因子和交通、城镇区位因子参与土地适宜性评价,利用层次分析法（AHP）获取各评价指标的权重值,然后运用多因子权重叠加分析模型对其城镇拓展空间土地适宜性进行评价,并根据适宜性大小划分出优化建设区、适宜建设区、限制建设区和禁止建设区4个等级。其中优化建设区面积235.75 km2,占总面积的9.51%;适宜建设区352.17 km2,占比14.21%;限制建设区1 643.92 km2,占比66.33%,禁止建设区218.54 km2,占比8.81%,另外还有水域27.99 km2,占比1.12%。     

GEO-Environmental Suitability Evaluation of Land for Urban Construction Based on A Back-Propagation Neural Network and GIS: A Case Study of Hangzhou

Evaluating the geo-environmental suitability of land for urban construction is an important step in the analysis of urban land use potential. Using geo-environmental factors and the land use status of Hangzhou, China, a back-propagation (BP) neural network model for the evaluation of the geo-environmental suitability of land for urban construction was established with a geographic information system (GIS) and techniques of grid, geospatial, and BP neural network analysis. Four factor groups, comprising nine separate subfactors of geo-environmental features, were selected for the model: geomorphic type, slope, site soil type, stratum steadiness, Holocene saturated soft soil depth, groundwater abundance, groundwater salinization, geologic hazard type, and geologic hazard degree. With the support of the model, the geo-environmental suitability of Hangzhou land for urban construction was divided into four suitability zones: zone I, suitable for super high-rise and high-rise buildings; zone II, suitable for multi-story buildings; zone III, suitable for low-rise buildings; and zone IV, not suitable for buildings. The results showed that a BP neural network can capture the complex non-linear relationships between the evaluation factors and the suitability level, and these results will support scientific decision-making for urban-construction land planning, management, and rational land use in Hangzhou.     

雷州半岛近岸海域水环境质量综合评价

采用单因子指数法、富营养化指数法和模糊综合评价法定量评价雷州半岛西岸海域水体环境质量,分析其影响因素,并提出污染防治对策。结果表明：（1）表层海水中的石油类和PO4-P两项评价指标严重超标,超标率达100%;部分站位的pH、Cr6等指标存在着超标现象。（2）除站位HND5外,海域水质均处于低贫营养化水平,且西南部海域水质富营养化程度小于东北部海域,部分站位PO4-P含量超出第三类水质,存在一定的污染危险。（3）从水质排序看,西部海域水环境质量高于东部海域。模糊隶属度高值区出现在乐民港、盐灶、江洪港和蛋场港附近,以及江洪港湾内和蛋场港湾内。（4）表层海水中部分检测项目超标是受近海作业、船舶油污水和上游污染物的影响;近海作业逐年增多,海洋捕捞、海产养殖发展迅猛,造成江洪港附近海域石油类、PO4-P和部分重金属超标严重;乐民港及盐灶附近水质较差可能是由于铁山港的污染物顺流而下所致。     

艾比湖流域水土开发的模糊综合评价

近年来随着水土开发活动的加剧,艾比湖流域已经成为我国干旱区生态环境严重退化的地区之一。水土资源利用的有效程度是区域水土资源可持续发展的前提与基础．直接关系水土资源的可持续利用。本文运用模糊综合评价模型．对艾比湖流域水土开发的有效性进行了评价。结果显示：艾比湖流域水资源开发利用率较高．平均达到75．99％;但水土资源有效利用率偏低．有效开发利用程度综合评分值只有0．316;水土资源开发处于低级及过渡阶段,工农业及经济属于耗水型．水资源综合管理水平低,但同时也说明水资源开发具有很大潜力。以评价结论为依据,给出了流域可持续发展水土资源保障的对策建议。     

基于AHP与模糊综合评价方法的南沙东部岛礁战略价值评价

南沙群岛是沟通太平洋与印度洋、联系亚欧大陆的交通要冲,具有重要的战略地位,在目前南沙众多岛礁被南海周边国家非法占据的背景下,控制未占领岛礁是维护我国主权和领土完整的有效手段,评价未占领岛礁的战略价值具有深远意义。研究基于AHP和模糊综合评价方法,选取五方礁、信义礁、海口礁、半月礁、舰长礁5座环礁为研究对象,通过建立评价要素指标体系,构建模糊综合评价模型,从主权价值、军事价值、航道控制价值和资源控制价值4个方面对岛礁战略价值进行综合评价,分析其战略意义和建设潜力结果显示,5个环礁的岛礁战略价值从高到低分别为五方礁（90.57）、半月礁（86.99）、信义礁（74.09）、舰长礁（65.94）、海口礁（58.12）,其中五方礁和半月礁战略价值最高,具有很大的发展潜力。     

基于AHP和模糊综合评判的玛纳斯河流域水资源安全评价

利用AHP（层次分析）和模糊综合评判法,结合2006年玛纳斯河流域土地利用和水资源数据库,并参考流域内各地自然、经济、社会统计资料,选取了4大类20个评价指标对水资源安全进行了综合评价。结果表明,玛纳斯河流域水资源处于不安全水平,其隶属度为0.4699,综合评分值为0.5291;生态安全指数和供需状况指数分别处于基本安全和比较安全水平,水资源量指数和社会经济指数皆处于不安全水平状态。因此,应提高流域的经济技术发展水平和水资源的利用效率,加强水资源的综合管理,控制人口增长,协调好上、下游之间及源流与干流之间的用水关系,以保障流域水资源的安全程度。     

Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States

The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km² as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.