共查询到20条相似文献，搜索用时 31 毫秒
1.
Michele Crosetto Stefano Tarantola 《International journal of geographical information science》2013,27(5):415-437
A novel procedure to analyse the uncertainty associated to the output of GIS-based models is presented. The procedure can handle models of any degree of complexity that accept any kind of input data. Two important aspects of spatial modelling are addressed: the propagation of uncertainty from model inputs and model parameters up to the model output (uncertainty analysis); and the assessment of the relative importance of the sources of uncertainty in the output uncertainty (sensitivity analysis). Two main applications are proposed. The procedure allows implementation of a GIS-based model whose output can reliably support the decision process with an optimized allocation of resources for spatial data acquisition. This is possible in low cost strategy, based on numerical simulations on a small prototype of the GIS-based model. Furthermore, the procedure provides an effective model building tool to choose, from a group of alternative models, the best one in terms of cost-benefit analysis. A comprehensive case study is described. It concerns the implementation of a new GIS-based hydrologic model, whose goal is providing near real-time flood forecasting. 相似文献
2.
As citizens take on expanded roles in gathering and reporting environmental data, their potential impact may relate to organizational traits. This study sought to understand the relationship through a survey that identified traits and impacts of U.S. volunteer water monitoring programs on natural resource policy and management. A multiple regression model tested the influence of nine traits on an index of impact, addressing eight a priori hypotheses related to natural resource management outcomes. Seven traits were significantly related to impacts. Significant positive relationships included: the objective to address an environmental crisis; an EPA and/or state-approved quality assurance plan; support of external decision makers who may use or benefit from data; larger budget; volunteers playing more roles in the research process. Fewer impacts were expected from programs operating within schools. Understanding these relationships can help guide citizen science programs or other types of citizen engagement efforts. 相似文献
3.
Mariana Madruga de Brito Adrian Almoradie Mariele Evers 《International journal of geographical information science》2019,33(9):1788-1806
This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems. 相似文献
4.
D. J. ABEL S. K. YAP R. ACKLAND M. A. CAMERON D. F. SMITH G. WALKER 《International journal of geographical information science》2013,27(3):193-204
Abstract Recent changes in information technology offer the opportunity to explore alternative architectures for geographical information systems (GIS) which might better support advanced applications. This paper describes the architecture and implementation of the environmental decision support system (EDSS), a prototype GIS tool kit. The architecture is based on a simple yet powerful systems model using only data collections, views and operations as the basic entity types. The design of the user interface, data management and data analysis within the model are outlined, with particular emphasis on the advanced facilities for which implementation is simplified by the architecture. A prototype applications system, BANKSIA, is also described. 相似文献
5.
从国家重建规划对资源环境承载能力应急评价的应用需求出发,解析重大灾害事件发生后承载能力应急评价难点,初步构建在灾后资源赋存、环境容量、生态安全以及灾害风险多重约束下,资源环境承载能力应急评价的基本范式与技术流程,并提出未来灾后承载能力评价及研究重点。研究表明：针对评价周期短、涉及内容广、决策风险高、不确定性强、数据基础不足等特点与难点,需从地域功能预估与指标体系、单项要素评价与技术准则、综合集成评价与重建分区、人口容量测算与重建模式4个方面实施灾后应急评价。未来,建议从灾后资源环境承载能力评价关键阈值与参数研究,应急评价辅助支撑体系与规划衔接技术研究,灾后承载能力演化特征与弹性机理研究,青藏高原边缘地带及近邻山区等灾害高风险区的重点研究与超前应对,以及灾后应急评价及重建规划的工作协调与应用推广等方面,进一步深化灾后资源环境承载能力研究,提升未来应急评价快速化、规范化、精准化水平,为增强国家和地方政府灾后系统应对能力提供借鉴。 相似文献
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电子政务数据资源的开发利用 总被引：8，自引：0，他引：8
如何使已有的政务数据实现有效共享并通过数据挖掘转变为知识和财富已经成为电子政务建设的一个关键内容。该文分析了电子政务数据资源开发利用的重要性，建议尽快制订政务元数据标准，以促进政务数据共享。提出了电子政务数据仓库的体系结构和建设内容，并且认为把分布式数据挖掘技术引入电子政务中对于政府决策支持意义重大。 相似文献
8.
Spatial uncertainty analysis is a complex and difficult task for orebody estimation in the mining industry. Conventional models (kriging and its variants) with variogram-based statistics fail to capture the spatial complexity of an orebody. Due to this, the grade and tonnage are incorrectly estimated resulting in inaccurate mine plans, which lead to costly financial decision. Multiple-point geostatistical simulation model can overcome the limitations of the conventional two-point spatial models. In this study, a multiple-point geostatistical method, namely SNESIM, was applied to generate multiple equiprobable orebody models for a copper deposit in Africa, and it helped to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by sequential Gaussian simulation within each equiprobable orebody models. The results were validated by reproducing the marginal distribution and two- and three-point statistics. The results show that deviations of volume of the simulated orebody models vary from ? 3 to 5% compared to the training image. The grade simulation results demonstrated that the average grades from the different simulation are varied from 3.77 to 4.92% and average grade 4.33%. The results also show that the volume and grade uncertainty model overestimates the orebody volume as compared to the conventional orebody. This study demonstrates that incorporating grade and volume uncertainty leads to significant changes in resource estimates. 相似文献
9.
Calvin A. Whealton Jery R. Stedinger Jared D. Smith Teresa E. Jordan Franklin G. Horowitz Maria C. Richards 《International journal of geographical information science》2020,34(10):2053-2076
ABSTRACT The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems. 相似文献
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There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations. 相似文献
12.
The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level. 相似文献
13.
Considering the important role played today by unconventional gas resources in North America and their enormous potential
for the future around the world, it is vital to both policy makers and industry that the volumes of these resources and the
impact of technology on these resources be assessed. To provide for optimal decision making regarding energy policy, research
funding, and resource development, it is necessary to reliably quantify the uncertainty in these resource assessments. Since
the 1970s, studies to assess potential unconventional gas resources have been conducted by various private and governmental
agencies, the most rigorous of which was by the United States Geological Survey (USGS). The USGS employed a cell-based, probabilistic
methodology which used analytical equations to calculate distributions of the resources assessed. USGS assessments have generally
produced distributions for potential unconventional gas resources that, in our judgment, are unrealistically narrow for what
are essentially undiscovered, untested resources. In this article, we present an improved methodology to assess potential
unconventional gas resources. Our methodology is a stochastic approach that includes Monte Carlo simulation and correlation
between input variables. Application of the improved methodology to the Uinta–Piceance province of Utah and Colorado with
USGS data validates the means and standard deviations of resource distributions produced by the USGS methodology, but reveals
that these distributions are not right skewed, as expected for a natural resource. Our investigation indicates that the unrealistic
shape and width of the gas resource distributions are caused by the use of narrow triangular input parameter distributions.
The stochastic methodology proposed here is more versatile and robust than the USGS analytic methodology. Adoption of the
methodology, along with a careful examination and revision of input distributions, should allow a more realistic assessment
of the uncertainty surrounding potential unconventional gas resources. 相似文献
14.
Aggrey Agumya Gary J. Hunter 《International journal of geographical information science》2013,27(5):405-417
The presence of uncertainty in geographical data has the potential to expose users to undesirable consequences in their decision making. Accordingly, our efforts to understand uncertainty seek to (a) avoid the use of data that are not suitable for their intended purpose (that is, data whose consequences are unacceptable), (b) to reduce any undesirable consequences to an acceptable level, and (c) to devise ways of living with undesirable data when the adverse consequences caused by poor data do not alter our ultimate decision choice. To assist this task, we propose an approach where the adverse consequences of uncertainty caused by the use of unsuitable geographical data are expressed in terms of risk. In this paper we first show that risk management offers geographical data users a range of options for responding to the adverse consequences of data uncertainty, and secondly we present and discuss the various risk response options. 相似文献
15.
TELLEZ Ricardo Delgado WANG Shaohua ZHONG Ershun CAI Wenwen LONG Liang 《资源与生态学报(英文版)》2016,7(6):430-438
This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms (CLG-LUSA). It uses Kohonen’s Self Organized Maps (SOM) and Linear Vector Quantization (LVQ) among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision’s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a municipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making. 相似文献
16.
L. Bingham A. Escalona D. Karssenberg 《International journal of geographical information science》2016,30(8):1552-1578
This article applies error propagation in a Monte Carlo simulation for a spatial-based fuzzy logic multi-criteria evaluation (MCE) in order to investigate the output uncertainty created by the input data sets and model structure. Six scenarios for quantifying uncertainty are reviewed. Three scenarios are progressively more complex in defining observational data (attribute uncertainty); while three other scenarios include uncertainty in observational data (position of boundaries between map units), weighting of evidence (fuzzy membership assignment), and evaluating changes in the MCE model (fuzzy logic operators). A case study of petroleum exploration in northern South America is used. Despite the resources and time required, the best estimate of input uncertainty is that based on expert-defined values. Uncertainties for fuzzy membership assignment and boundary transition zones do not affect the results as much as the attribute assignment uncertainty. The MCE fuzzy logic operator uncertainty affects the results the most. Confidence levels of 95% and 60% are evaluated with threshold values of 0.7 and 0.5 and show that accepting more uncertainty in the results increases the total area available for decision-making. Threshold values and confidence levels should be predetermined, although a series of combinations may yield the best decision-making support. 相似文献
17.
国土资源知识库的建设与应用 总被引：2，自引：0，他引：2
为满足决策分析的需要和指导国土资源数据利用,从数据—知识—应用3个层次对国土资源数据进行了深层次的挖掘和利用,按照知识获取→知识表示→知识库设计、存储→知识利用技术流程建设国土资源知识库,包括国土资源空间知识库、国土资源影像知识库、国土资源专题知识库、国土资源元知识库,并给出知识库应用于国土资源综合分析系统的实例。 相似文献
18.
Jason A. McLennan Weishan Ren Oy Leuangthong C. V. Deutsch 《Natural Resources Research》2006,15(2):119-127
The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in
the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the
stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty
in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture
geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations.
A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the
recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB)
and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB
and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces
to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort
McMurray, Alberta. 相似文献
19.
多灾种综合风险评估软层次模型 总被引：2，自引：1，他引：1
在复杂的灾害风险系统中,风险并非简单相加,但目前的研究成果基本是单一灾种简单相加得到的综合风险,缺乏可靠性。因此,此研究基于灾害风险系统理论,引入模糊信息粒化方法和模糊转化函数,利用模糊近似推理理论和方法,建立一个多灾种综合风险评估软层次模型。研究表明该模型的优势：1）不仅考虑了灾害风险系统中的确定性,而且还包括了随机不确定性和模糊不确定性;2）利用模糊信息粒化方法不仅减少了数据的不确定性,而且还包括了一些主观信息,使得评估结果更加接近实际,理论与实际紧密结合,更有利于风险管理者和决策者为减少损失规避风险提供依据;3）通过模糊转化函数将不同灾种得到的不同量纲的量转化同一量纲的量,以便于综合分析和模糊近似推理,获得多灾种综合风险。以云南省丽江地区（市）的地震－洪水灾害为例,验证多灾种综合风险评估软层次模型的实用性,并将其结果与世界银行灾害管理中心和哥伦比亚大学灾害和风险研究中心所建议的风险评估模型（HMU－CHRR模型）的结果进行比较分析,讨论了本研究所建模型在多灾种综合风险评估中的特点。 相似文献