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1.
基于GIS的防灾适宜度多准则评价(MCE)是土地利用防灾规划的关键。根据唐山市地质灾害资料,建立基于距离的防灾适宜度评价准则并量化评价指标。依据决策风险指标计算次序权重,应用层次分析程序(AHP)构建比较矩阵并计算准则权重,分析基于GIS的OWA方法、布尔决策和权重线性叠加(WLC)等多准则评价方法的决策风险,确定唐山市土地利用防灾评价策略。基于决策风险和指标补偿原则计算次序权重、准则权重和一致性比率,得到唐山市土地利用防灾适宜度评价结果,据此提出唐山市土地资源合理利用建议。  相似文献   

2.
Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.  相似文献   

3.
A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping   总被引：1，自引：0，他引：1  
This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.  相似文献   

4.
An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the unknown gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere.  相似文献   

5.
《自然地理学》2013,34(2):130-153
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.  相似文献   

6.
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.  相似文献   

7.
朱磊  盛建东  贾宏涛 《干旱区地理》2019,42(5):1115-1123
准确、高效地掌握草原土壤属性的空间分布能够为草地资源境管理提供基础信息和参考依据。相比于传统土壤调查方法，基于模糊逻辑的土壤—环境推理能够提高野外采样效率和预测制图精度，被广泛应用于数字土壤制图。但由于土壤自身的空间变异性及其与环境条件间的非线性，现有推理模型的稳定性较低，尚未在高寒草原区进行应用。选择新疆巴音布鲁克典型亚高山草原地区约4 km2区域为研究区，以高程、坡度、坡向、沿剖面曲率、沿等高线曲率、地形湿度指数6个地形因子为土壤环境因子，采用模糊[WTBX]C[WTBZ]均值聚类（Fuzzy C-means Clustering，FCM）方法对环境因子聚类，得到9个环境因子组合，并在隶属度值高的环境因子组合中心共设置18个典型点。运用土壤—环境推理方法模拟研究区表层土壤pH值空间分布，其变化范围在7.170~8.186之间。选取35个独立样本进行精度检验（均匀采样点16个，横截面采样点9个，垂直带采样点10个），模拟结果与实测值基本吻合，且基于模糊聚类和土壤—环境推理方法的模拟精度高于普通克里格法和反距离权重法。通过基于模糊逻辑和土壤—环境推理的数字土壤制图方法在小尺度区域的运用验证，结果表明基于典型点的采样方案能够快速、有效地对区域土壤属性进行空间模拟，该方法对于类似小尺度的研究区同样有效。  相似文献   

8.
Agronomic soil management and decision-making frequently requires the joint classification of soil variables. Fuzzy set theory is often used to accomplish this task. This paper addresses the issues of objectively defining fuzzy membership functions (FMF) and reducing classification uncertainty with hedge operators. As an example, soil in North-east Thailand was classified according to its inherent potential to support the recovery of a rice crop after a drought spell. The utility of auxiliary information not directly included in the classification was explored. A tree cover density index was employed for an objective definition of the FMF to classify soil organic matter content and plant-available potassium. Mapping units were allocated to classes having low, medium or high availability of these plant nutrients. It was shown that crisp, Boolean style classifications severely misclassify land in all but one class. Adjusted FMF decreased the uncertainty contained in thematic class maps. Single FMF values for soil organic matter and plant-available K were then jointly modelled and the soil classified as having low, medium and high potential for rice plants to recover from drought impacts. The very and more or less hedge operators were applied to increase or decrease the joint FMF values using farmer' knowledge about soil fertility. Overall classification uncertainty using FMF was decreased by 14% if the standard FMF was adjusted and the generated membership values were hedged. It was shown that adjusting FMF influenced the uncertainty components vagueness and ambiguity differently; the former increased slightly but the latter was drastically reduced.  相似文献   

9.
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.  相似文献   

10.
ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.  相似文献   

11.
Many real-world spatial planning and management problems give rise to a geographical information system (GIS)-based multi-criteria decision-making. Analytical network process (ANP) provides a comprehensive methodology for representing complex multi-criteria decision-making problems as a network of criteria and alternatives, where feedback and interdependence relationships may exist within and between all the criteria and alternatives. Experts’ experiences are used to estimate relative magnitudes of tangible and intangible factors through paired comparisons in order to make rational and consistent decisions. However, the GIS-based ANP, an adoption of weighted linear aggregation rule, typically employed a high trade-off decision strategy and neglected other decision strategies. This paper develops a novel GIS-based multi-criteria evaluation (MCE) procedure by extending the ANP using fuzzy quantifiers-guided ordered weighted averaging (OWA) operators. This extension, which generalizes the aggregation process used in the ANP, would provide a generic powerful decision-making tool that allows decision-makers to define a decision strategy on a continuum between pessimistic (risk-averse) and optimistic (risk-taking) strategies. By changing the linguistic quantifiers, the GIS-based ANP–OWA can generate a wide range of decision strategies taking into accounts the level of risk the decision-makers wish to assume in their MCE. A land-use suitability analysis in a region of Saudi Arabia is presented to demonstrate the application of the proposed procedure.  相似文献   

12.
The paper investigates whether the methods chosen for representing uncertain geographic information aid or impair decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly difficult tasks, this research evaluates the effect of five different visualizations and a text-based representation on decision-making under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little difference to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing difficulty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were among the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.  相似文献   

13.
基于GIS栅格数据的空间模糊综合评判方法与实践   总被引：3，自引：0，他引：3  
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。  相似文献   

14.
A new methodology for evaluating coastal scenery: fuzzy logic systems   总被引：1，自引：0，他引：1  
Coastal scenery evaluated by utilization of selected landscape components was subjected to a fuzzy logic systems approach. Twenty-six top-rated parameters were identified from a literature search/questionnaire surveys carried out in Malta, Turkey and the UK and coastal scenery investigated at 57 sites. A coastal scenic evaluation checklist system was finalized and consisted of assessment parameters based on a five-point scale ranging from low to high attribute values. Coastal user parameter preferences and priorities for these parameters were obtained by a questionnaire perception study (n = 270) given to both locals and visitors. Assessment parameter weights were calculated from this public perception survey via matrices relating to the selected landscape components. For each of the 26 parameters, a membership-graded matrix was established to counteract potential errors in assigning grades to the parameters when one has to give a unique number to the attribute value. To overcome subjectivity and quantify uncertainty, fuzzy logic mathematical methodology was adopted to this checklist approach. This enabled an Evaluation Index (D) value to be calculated, establishing a 5-class evaluation system. Class 1 scenery (extremely attractive natural site) had D values > 0.85; Class 2, between 0.85 and 0.65; Class 3, between 0.65 and 0.4; Class 4, between 0.4 and zero; Class 5 (very unattractive, intensively developed urban) below zero.  相似文献   

15.
The inherent problems of classifying or inventorying potential mineral resources (as opposed to known mineral resources) pose specific challenges. In this paper, the application of a conceptual mineral exploration model and GIS to generate mineral potential maps as input to land-use policy decision-making is illustrated. We implement the criteria provided by a conceptual exploration model for nickeliferous-laterites by using a GIS to classify the nickeliferous-laterite potential of an area in the northeastern part of the Philippines. The spatial data inputs to the GIS are geological map data, topographic map data, and stream sediment point data. Processing of these data yields derivative maps, which are used as indicators of nickeliferous-laterite potential. The indicator maps then are integrated to furnish a nickeliferous-laterite potential map. This map is compared with present land-use classification and policy in the area. The results indicate high potential for nickeliferous-laterite occurrence in the area, but the zones of potential are in places where mineral resources development is prohibited. The prohibition was imposed before the nickeliferous-laterite potential was assessed by this study. Mineral potential classification therefore is a critical input to land-use policy-making so that prospective land is not alienated from future mineral resource development.  相似文献   

16.
In this study, we propose, describe, and demonstrate a new geovisualization tool to demonstrate the use of exploratory and interactive visualization techniques for a visual fuzzy classification of remotely sensed imagery. The proposed tool uses dynamically linked views, consisting of an image display, a parallel coordinate plot, a 3D feature space plot, and a classified map with an uncertainty map. It allows a geoscientist to interact with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions and fuzzy transition zones of land-cover classes. The purpose of this tool is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. We tested our tool with a visual fuzzy land-cover classification of a Landsat 7 ETM+ image of an area in southern France characterized by objects with indeterminate boundaries. Good results were obtained with the visual classifier. Additionally, a focus-group user test of the tool showed that insight into a fuzzy classification algorithm and classification uncertainty improved considerably.  相似文献   

17.
An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.  相似文献   

18.
A fundamental task for petroleum exploration decision-making is to evaluate the uncertainty of well outcomes. The recent development of geostatistical simulation techniques provides an effective means to the generation of a full uncertainty model for any random variable. Sequential indicator simulation has been used as a tool to generate alternate, equal-probable stochastic models, from which various representations of uncertainties can be created. These results can be used as input for the quantification of various risks associated with a wildcat drilling program or the estimation of petroleum resources. A simple case study is given to demonstrate the use of sequential indicator simulation. The data involves a set of wildcat wells in a gas play. The multiple simulated stochastic models are then post-processed to characterize various uncertainties associated with drilling outcomes.  相似文献   

19.
Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning.  相似文献   

20.
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   

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