水资源系统中的不确定性及风险分析方法

水资源系统中广泛存在着不确定性，其对系统的影响很大，甚至带来灾害性风险(如洪水、干旱)，是风险产生的根本原因，也是水资源系统研究遇到的难点问题之一。从分析总结水资源系统中存在的不确定性因素的类型(包括随机性、模糊性、灰色性及未确知性)及研究方法，提出水资源不确定性系统的概念。从不确定性因素的数学处理方法入手，介绍基于概率统计学(处理随机性)的风险计算模型，提出基于模糊隶属度(处理模糊性)、基于灰数(处理灰性)和基于未确知数(处理未确知性)的风险计算模型。为水资源系统风险规划与管理奠定基础。     

基于场所的GIS中的不确定性问题及分类框架

基于场所的GIS直接表达人类地理空间知识的管理和加工过程,而不确定性是人类智能的基本特点,因此GIS的智能化需要研究其中的不确定性问题。与传统的GIS相比,基于场所的GIS中的不确定性问题更为丰富,既包括随机性,也包括含糊性,而不确定性的主体既可以是地理要素、场所和空间关系,也包括命题和规则。该文介绍该领域相关的研究成果,基于不确定性主体、类型、表达手段及相关的活动4个视角,建立了基于场所的GIS中所涉及的不确定性框架,从而为相关的不确定性建模提供指导。     

空间数据关联规则挖掘的不确定性处理及度量

针对传统空间数据关联规则挖掘缺乏不确定性处理及度量的局限性,将空间数据的不确定性和空间数据挖掘的不确定性有机结合,初步建立了空间数据关联规则挖掘的不确定性处理模型及度量指标,包括空间数据不确定性的Monte Carlo模拟、基于不确定性空间数据的空间自相关度量和关联规则不确定性度量等,并以我国某地区环境调查数据为例进行验证。     

Modelling uncertainty in natural resource analysis using fuzzy sets and Monte Carlo simulation: slope stability prediction

The techniques of fuzzy logic and Monte Carlo simulation are combined to address two incompatible types of uncertainty present in most natural resource data: thematic classification uncertainty and variance in unclassified continuously distributed data. The resultant model of uncertainty is applied to an infinite slope stability model using data from Louise Island, British Columbia. Results are summarized so as to answer forestry decision support queries. The proposed model of uncertainty in resource data analysis is found to have utility in combining different types of uncertainty, and efficiently utilizing available metadata. Integration of uncertainty data models with visualization tools is considered a necessary prerequisite to effective implementation in decision support systems.     

Forthcoming events

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

利用场模型拓扑表达GIS中的地理目标

该文阐述为了实现在GIS中描述自然界里带有模糊不确定性的地理目标 ,扩充GIS的模糊查询功能 ,首先基于集合论思想探讨了确定性地理目标的代数解析表达与其拓扑表达 ,指出了确定性点、线、面之间拓扑表达的构成机理 ;然后 ,基于场模型建立了模糊地理目标的空间表达 ,分析了模糊地理目标的位置不确定性 ,进而建立了模糊地理目标的拓扑表达模型 ,即点集拓扑内部、边界和外部。分析表明 ,公认的Egenhofer模型是该文模型在地理目标不带有误差或不确定性情况下的特例。最后 ,与Clementini(1996 )提出的模型做了比较分析 ,表明了该文模型的合理性。     

Prediction of Aquifer Vulnerability to Pesticides Using Fuzzy Rule-Based Models at the Regional Scale

Contamination of ground water has been a major environmental concern in recent years. The potential for ground-water contamination by pesticides depends on porous media, solute, and hydrologic parameters. Although sophisticated deterministic computer models are available for assessing aquifer-contamination potential on a site-by-site basis, most deterministic models are too complex for vulnerability assessment on a regional scale because they require input data that are spatially and temporally variable, and which may not be available at this scale. Therefore, development of an affordable model that is robust under conditions of uncertainty at the watershed scale with minimum input of field data becomes a useful ground-water management tool. The purpose of this study was to examine the usefulness of fuzzy rule-based techniques in predicting aquifer vulnerability to pesticides at the regional scale. The objectives were to (1) develop fuzzy rule-based models using the same input parameters contained in an index-based model (i.e., the modified DRASTIC model), (2) determine the sensitivity of fuzzy rule model predictions, (3) compare the outputs of the fuzzy rule-based models with those of the modified DRASTIC model and with the results of aquifer water-quality analyses, and (4) examine the spatial variability of field parameters around contaminated wells of the Alluvial aquifer in Woodruff County Arkansas. The fuzzy rule-based model for objective (1) was developed using similar parameter weights and ratings as the modified DRASTIC model. For objective (2), fuzzy rule-based models were created using fewer parameters than the modified DRASTIC model. Sensitivity of the fuzzy rule-based models was determined using different combinations of weights of the four input parameters in DRASTIC. It was found that variations in the weights of the input parameters and number of fuzzy sets influenced the location of the aquifer-vulnerability categories as well as the area within each fuzzy category. The fuzzy rule models tended to predict somewhat higher vulnerabilities of the Alluvial aquifer than the modified DRASTIC model. The fuzzy rule base that had the soil-leaching index (S) as the highest weight was chosen as the best fuzzy rule model in predicting potential contamination by pesticides of the aquifer. In general, the fuzzy rule models tended to overestimate the vulnerability of the aquifer in the study area.     

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.     

Modelling collective Yawuru values along the foreshore of Roebuck Bay,Western Australia using fuzzy logic

The cadastral model has played a key role in Indigenous dispossession in settler states. Yet, the recognition of Indigenous land rights, which has increased globally since the 1960s, frequently requires Indigenous communities to directly engage with this spatial model. In Australia, native title claimants must use entity-based models of space to delineate their traditional territories during the claim process. They must also engage with planning and development documents which use the cadastral model of space to assert and defend their rights following native title recognition. This is often problematic as Indigenous spatial ontologies emphasise complexity and continuity which is inimical to the ‘crisp’ representations of cadastral space.This study explores the potential of a fuzzy index modelling approach to represent cultural values using a case study from Broome, Western Australia. Sketch mapping, fuzzy index modelling and combinatory techniques were used to produce a model of several cultural values held by the Yawuru community for the in-town foreshore of Roebuck Bay. This model was overlaid on local planning documents to provide the Yawuru community with strategic intelligence for post native title governance. The experience of co-producing this model suggests that such techniques may assist Indigenous communities to engage with settler structures. This implies that policies which fail to extend analytical capacity to interested native title groups as part of programmes of spatial enablement continue to perpetuate historical processes of colonial domination.     

长沙市人居环境与经济协调发展的不确定性评价(英文)

The coordinated development of human settlement environment and economy is of vital significance to urban sustainable development and urban ecosystem health. Urban human settlement and economic systems exist in urban ecosystems, which are a structural complexity. Therefore the research is being challenged by some uncertain factors between human settlements and economic systems. However most of the researches were focused on its determinate objective aspects and qualitative analyses while less concern on the quantitative evaluation of coordinated development of urban human settlement environment and economy, especially little on its uncertain aspect. At present, the urgent task is to study the coordinated development of urban settlement environment and economy in terms of the effect of uncertainty. This study analyzed the uncertain characteristics, which would be confronted at different stages, such as confirming the index categories, their bound values, and their construction rate, etc. According to the actual urban conditions, many construction principles based on uncertainties are put forward and an indicating system for human settlement and economic evaluation is established. Moreover, the application of fuzzy mathematics presents a new method and a calculation model for the comprehensive assessment of the coordinated development of urban human settlement environment and economy. The application of the method and model in Changsha city of China showed that the assessment results can reflect not only the overall coordination degree of the city, but also the mode of interactive mechanism between urban economic system and human settlement environment.     

Optimal spatial allocation of irrigation water under uncertainty using the bilayer nested optimisation algorithm and geospatial technology

The optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.     

大尺度水循环模拟系统不确定性研究进展(英文)

The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions.And the hydrological models play a very important role in simulating the complex system.However,there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty.The uncertainties in hydrological modeling come from four important aspects:uncertainties in input data and parameters,uncertainties in model structure,uncertainties in analysis method and the initial and boundary conditions.This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources.Also,the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out.And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced,which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models.Finally,some future perspectives on uncertainty quantification are put forward.     

SPATIAL SCALE INFLUENCES ON MODELED RUNOFF FOR LARGE WATERSHEDS

Modeling the runoff subsystem for large drainage basins requires simplification in representing natural processes owing to the spatial variability of mass and energy transfers in a watershed. Many models represent temporally and spatially distributed watershed variables by some form of aggregation or lumping. Scale becomes a factor in the lumping decision because some of the variables are spatially continuous while others are spatially discontinuous. The effects of scale differences on lumping in large watersheds is examined using the 27,300 km² drainage area of the Deschutes River, Oregon. The watershed is modeled using three different spatial aggregations for representing the runoff subsystem. Agreement between modeled and observed monthly runoff for water years 1951-60 is analyzed to evaluate the magnitude of the difference between corresponding observed and modeled values. Increasing the number of spatial units in the model from 1 to 9 reduces all of the error terms by about 35 percent, but 20 spatial units in the model reduce the error terms by 50 percent or more. These data provide explicit evidence of improved modeling accuracy achieved by employing a disaggregated watershed model to represent spatially heterogeneous conditions in a large drainage basin. [Key words: watershed modeling, scale, large watersheds, hydroclimate, Deschutes Basin.]     

不确定性视角下的中国省际人口迁移机制分析

人口迁移过程具有内在的不确定性。贝叶斯模型平均方法（BMA）为不确定性问题提供了行之有效的解决方案。然而,当前该方法多用于线性回归模型在变量选择时出现的模型不确定性问题,很少用于空间建模。本文以2010—2015年中国省际人口迁移流为例,将BMA方法应用于空间OD模型,在考虑网络空间结构的基础上选取迁出地和迁入地各7个解释变量及距离因素,利用马尔可夫链—蒙特卡罗模型综合方法（MC³）进行模型抽样,以后验模型概率为权重计算相应变量的迁出地、迁入地和网络效应等,定量分析不确定性背景下省际人口迁移影响因素和空间机制。结果表明：① BMA模型估计结果更为稳健可靠。与单一模型相比,BMA中变量效应估计的90%可信区间明显缩小,不确定性程度显著降低,结果更为精确;② 区域经济社会发展对省际迁移至关重要。经模型空间抽样后,迁出地人口规模和GDP、迁入地教育水平和迁移存量等的变量后验包含概率大于90%;③ 网络效应在省际迁移过程中不可忽视。所有变量的网络效应占总体效应的40%以上,其中工资、城镇化率、教育和迁移存量等的网络效应（绝对值）大于各自的迁出地和迁入地效应;④ 若不考虑迁移建模中的不确定性,绝大多数区域经济社会变量对省际迁移的影响会被高估。     

Mining significant crisp-fuzzy spatial association rules

Spatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.     

基于粗集的知识发现与地理模拟——以深圳市土地利用变化为例

为了更有效地模拟地理现象的复杂演变过程,提出了用粗集理论来确定元胞自动机 (CA)不确定性转换规则的新方法。CA可以通过局部规则来有效地模拟许多地理现象的演变过程。但目前缺乏很好定义CA转换规则的方法。往往采用启发式的方法来定义CA的转换规则,这些转换规则是静态的,而且其参数值多是确定的。在反映诸如城市扩张、疾病扩散等不确定性复杂现象时,具有一定的局限性。利用粗集从GIS和遥感数据中发现知识,自动寻找CA的不确定性转换规则,基于粗集的CA在缩短建模时间的同时,能提取非确定性的转换规则,更好地反映复杂系统的特点。采用所提出的方法模拟了深圳市的城市发展过程,取得了比传统MCE方法更好的模拟效果。     

Using multiple scale spatio-temporal patterns for validating spatially explicit agent-based models

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this article, we propose the use of multiple scale spatio-temporal patterns for validating spatially explicit ABMs. We evaluated several specifications of vector-borne disease transmission models by comparing space-time patterns of model outcomes to observations at multiple scales via the sum of root mean square error (RMSE) measurement. The results indicate that specifications of the spatial configurations of residential area and immunity status of individual humans are of importance to reproduce observed patterns of dengue outbreaks at multiple space-time scales. Our approach to using multiple scale spatio-temporal patterns can help not only to understand the dynamic associations between model specifications and model outcomes, but also to validate spatially explicit ABMs.     

降雨的空间不均性对模拟产流量和产沙量不确定的影响

在传统的水文/水质模型中,降雨被认为是在空间上均匀分布并且对模型输出的不确定性不产生影响。本文的目的是评价由于降雨的空间分布不均匀性对模型输出-产流量和产沙量-不确定性的影响。本文选取卢氏流域为研究区域,使用SWAT模型和流域内24个雨量站的降雨作为模型的输入。基于降雨空间分布均匀的假定下,每次用一个雨量站的点雨量来作为流域的面平均降雨量,模拟的产流量和产沙量的不确定性来自于降水的不均匀性。模拟的产流量和产沙量的不确定性大于降雨的不确定性,结果表明,在运用水文/水质模型时,为了准确的模拟、预测产流量和产沙量必须掌握降雨的空间分布特性并将其应用于水文、水质模拟之中。