CRITICAL DATA REQUIREMENTS FOR PREDICTION OF EROSION AND SEDIMENTATION IN MOUNTAIN DRAINAGE BASINS1

ABSTRACT: The potential for understanding and, where necessary, managing sedimentation in humid mountain drainage basins increases with awareness of the conditions that lead to shallow landsliding, debris flows, and catastrophic sedimentation in stream channels. Progress in understanding has involved: improved recognition of source areas and the potential for downstream effects of slope failure; improved understanding of hydrological conditions required for failure; and a general theory of slope stability in shallow colluvium, including the role of plants, fires, timber harvest, and other disturbances. The theory acknowledges spatial variability in topographic and geotechnical terrain characteristics, the stochastic nature of climatic triggering events such as forest fires and rainstorms, and the integrating nature of channel networks in modulating the cumulative effects of transient processes within a basin. Anthropogenic fire regimes, road effects, and timber harvest can readily be included. Continued application and modification of the theory over an expanded geographical range require improvements in field data and their systematic storage in spatial databases. Improvements in digital topographic data for mountain basins, systematic network-wide surveys of channel conditions, and new technology for rapid documentation of soil depths in landslide source areas would enhance the prediction of mass failure, its consequences for channel habitat, and the basin-wide or regional distribution of hillslope and channel conditions. Computations of the probabilities of transient effects throughout basins could then form the basis of ecological risk analyses. Large-scale spatial data sets of a few critical variables are required before this next level of understanding can be developed and applied to sedimentation impacts on ecosystems and other resources.     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

模糊数学在环保基金投资效益分析中的应用

从模糊分析的基本概念出发，采用“主因素决定型：的综合评综合方法，提出了环保基金效益分析模糊隶属度原则，建立了保投资效益分析的模糊数学模型，通过实例分析表明：（１）环保基金投分析应选择污染投资费比，污染物削减费比，污染物处理效益和污染物去除率４个综合指标；（２）应用模型的预测功能，对年度早报的污染物治理项目优劣进行效益分析筛选，排序，为科学合理的编制年度计划提供依据。     

大气质量评价上海指数法的改进及应用

该研究将模糊数学应用于上海指数法，在评价函数中引入半梯形隶属函数和倍斜率隶属函数，提出模糊上海指数法，将此法应用于延安市的大气质量评价工人,欧几里德贴近度法和上海指法的结果进行比较表明，模糊上海指数法的评价结果是科学合理的，并利用地理信息系统Mapinfo制作出基于地理信息的城市大气质量评价图，使评价结果可视化，地图化。     

模糊数学方法在城市环境综合整治定量考核综合评判中的应用

本文把模糊数学方法用于城市环境质量综合评判中,较好地处理了考核指标等级间的模糊性;由于最大隶属度原则损失信息太多,这种原则不能用于城市环境质量综合评判,而必须利用各等级隶属度提供的全部信息,把隶属度作为“权”参予计分,模糊数学法较直接计分法明显地提高了判别灵敏度,对城市环境质量作出了客观的评价.     

模糊聚类分析洁在哈密市环境噪声质量评价中的应用

采用模糊聚类分析方法．对哈密市多年来城市环境噪声监测数据，进行了质量评价。结果表明，该市受到噪声中污染的Ⅳ级水平，提出了相应建议。     

应用模糊综合指数法综合评价额尔齐斯河水质

应用模糊综合指数法对额尔齐斯河水质进行综合评价，指出额尔齐斯河水质已轻度污染，管理不善是额尔齐斯河水质下降的主要原因。     

A two step fuzzy model for the assessment and ranking of organizational resilience factors in the process industry

The evaluation and ranking of resilience factors (RFs) in an uncertain environment has important implications for the management of any enterprise. Determining an improvement strategy of business process resilience is based on the obtained rank of RFs, and it presents a key success factor for an enterprise in dealing with crisis. The complexity and importance of the treated problem calls for analytic methods rather than intuitive decisions. The relative importance of business processes and the relative importance of RFs under each business process are stated by fuzzy pair-wise comparison matrices. The elements of these matrices are triangular fuzzy numbers (TFNs). The fuzzy Analytic Hierarchy Process (FAHP) is used for determination of relative weights of existing variables. The rank of RFs is obtained by using the extent fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The real life application on the selection of the management team shows the practical implications in the process industry.     

Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models

The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of environmental modelling from physical relations to expert knowledge. In such applications the expert describes the system in terms of fuzzy variables and inference rules. The result of the fuzzy reasoning process is a numerical output value. In such a model, as in any other, the model context, structure, technical aspects, parameters and inputs may contribute uncertainties to the model output. Analysis of these contributions in a simplified model for agriculture suitability shows how important information about the accuracy of the expert knowledge in relation to the other uncertainties can be provided. A method for the extensive assessment of uncertainties in compositional fuzzy rule-based models is proposed, combining the evaluation of model structure, input and parameter uncertainties. In an example model, each of these three appear to have the potential to dominate aggregated uncertainty, supporting the relevance of an ample uncertainty approach.     

高风险移动放射源风险管控模糊综合评价

根据高风险移动放射源辐射安全风险特点与辐射事故经验反馈,结合辐射安全监管部门和企业实际,在充分调研的基础上,构建高风险移动放射源辐射安全风险管控评价指标体系,体系分为目标层、准则层和方案层,准则层8项指标,方案层40项指标。采用层次分析法确定各指标权重,运用模糊综合评价方法对甘肃省7家高风险移动放射源企业进行评价,结果表明决策层指标权重较大,与实际情况相符。