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1.
Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP 总被引：10，自引：1，他引：9
Qiang Zou Jianzhong Zhou Chao Zhou Lixiang Song Jun Guo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(2):525-546
On the basis of the disaster system theory and comprehensive analysis of flood risk factors, including the hazard of the disaster-inducing factors and disaster-breeding environment, as well as the vulnerability of the hazards-bearing bodies, the primary risk assessment index system of flood diversion district as well as its assessment standards were established. Then, a new model for comprehensive flood risk assessment was put forward in this paper based on set pair analysis (SPA) and variable fuzzy sets (VFS) theory, named set pair analysis-variable fuzzy sets model (SPA-VFS), which determines the relative membership degree function of VFS by using SPA method and has the advantages of intuitionist course, simple calculation and good generality application. Moreover, the analytic hierarchy process (AHP) was combined with trapezoidal fuzzy numbers to calculate the weights of assessment indices, thus the weights for flood hazard and flood vulnerability were determined by the fuzzy AHP procedure, respectively. Then SPA-VFS were applied to calculate the flood hazard grades and flood vulnerability grades with rank feature value equation and the confidence criterion, respectively. Under the natural disasters risk expression recommended by the Humanitarian Affairs Department of United Nations, flood risk grades were achieved from the flood hazard grades and flood vulnerability grades with risk grade classification matrix, where flood hazard, flood vulnerability and flood risk were all classified into five grades as very low, low, medium, high and very high. Consequently, integrated flood risk maps could be carried out for flood risk management and decision-making. Finally, SPA-VFS and fuzzy AHP were employed for comprehensive flood risk assessment of Jingjiang flood diversion district in China, and the computational results demonstrate that SPA-VFS is reasonable, reliable and applicable, thus has bright prospects of application for comprehensive flood risk assessment, and moreover has potential to be applicable to comprehensive risk assessment of other natural disasters with no much modification. 相似文献
2.
Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand 总被引：1，自引：0，他引：1
A comprehensive 32 kHz multibeam bathymetry and backscatter survey of Cook Strait, New Zealand (∼8500 km2), is used to generate a regional substrate classification map over a wide range of water depths, seafloor substrates and geological landforms using an automated mapping method based on the textural image analysis of backscatter data. Full processing of the backscatter is required in order to obtain an image with a strongly attenuated specular reflection. Image segmentation of the merged backscatter and bathymetry layers is constrained using shape, compactness, and texture measures. The number of classes and their spatial distribution are statistically identified by employing an unsupervised fuzzy-c-means (FCM) clustering algorithm to sediment samples, independent of the backscatter data. Classification is achieved from the overlay of the FCM result onto a segmented image and attributing segments with the FCM class. Four classes are identified and uncertainty in class attribution is quantified by a confusion index layer. Validation of the classification map is done by comparing the results with the sediment and structural maps. Backscatter (BS) strength angular profiles are used to show acoustic class separation. The method takes us one step further in combining multibeam data with physical seabed data in a complementary analysis to seek correlations between datasets using object-based image analysis and unsupervised classification. Texture within these identified classes is then examined for correlation with typical backscatter angular responses for mud, sand and gravel. The results show a first order correlation between each of the classes and both the sedimentary properties and the geomorphological map. 相似文献
3.
E. Aghaarabi F. Aminravan R. Sadiq M. Hoorfar M. J. Rodriguez H. Najjaran 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):655-679
This paper presents the use of two multi-criteria decision-making (MCDM) frameworks based on hierarchical fuzzy inference engines for the purpose of assessing drinking water quality in distribution networks. Incommensurable and uncertain water quality parameters (WQPs) at various sampling locations of the water distribution network (WDN) are monitored. Two classes of WQPs including microbial and physicochemical parameters are considered. Partial, incomplete and subjective information on WQPs introduce uncertainty to the water quality assessment process. Likewise, conflicting WQPs result in a partially reliable assessment of the quality associated with drinking water. The proposed methodology is based on two hierarchical inference engines tuned using historical data on WQPs in the WDN and expert knowledge. Each inference engine acts as a decision-making agent specialized in assessing one aspect of quality associated with drinking water. The MCDM frameworks were developed to assess the microbial and physicochemical aspects of water quality assessment. The MCDM frameworks are based on either fuzzy evidential or fuzzy rule-based inference. Both frameworks can interpret and communicate the relative quality associated with drinking water, while the second is superior in capturing the nonlinear relationships between the WQPs and estimated water quality. More comprehensive rules will have to be generated prior to reliable water quality assessment in real-case situations. The examples presented here serve to demonstrate the proposed frameworks. Both frameworks were tested through historical data available for a WDN, and a comparison was made based on their performance in assessing levels of water quality at various sampling locations of the network. 相似文献
4.
The seismic reflection method provides high-resolution data that are especially useful for discovering mineral deposits under deep cover. A hindrance to the wider adoption of the seismic reflection method in mineral exploration is that the data are often interpreted differently and independently of other geophysical data unless common earth models are used to link the methods during geological interpretation. Model-based inversion of post-stack seismic data allows rock units with common petrophysical properties to be identified and permits increased bandwidth to enhance the spatial resolution of the acoustic-impedance model. However, as seismic reflection data are naturally bandlimited, any inversion scheme depends upon an initial model, and must deal with non-unique solutions for the inversion. Both issues can be largely overcome by using constraints and integrating prior information. We exploit the abilities of fuzzy c-means clustering to constrain and to include prior information in the inversion. The use of a clustering constraint for petrophysical values pushes the inversion process to select models that are primarily composed of several discrete rock units and the fuzzy c-means algorithm allows some properties to overlap by varying degrees. Imposing the fuzzy clustering techniques in the inversion process allows solutions that are similar to the natural geologic patterns that often have a few rock units represented by distinct combinations of petrophysical characteristics. Our tests on synthetic models, with clear and distinct boundaries, show that our methodology effectively recovers the true model. Accurate model recovery can be obtained even when the data are highly contaminated by random noise, where the initial model is homogeneous, or there is minimal prior petrophysical information available. We demonstrate the abilities of fuzzy c-means clustering to constrain and to include prior information in the acoustic-impedance inversion of a challenging magnetotelluric/seismic data set from the Carlin Gold District, USA. Using fuzzy c-means guided inversion of magnetotelluric data to create a starting model for acoustic-impedance proved important in obtaining the best result. Our inversion results correlate with borehole data and provided a better basis for geological interpretation than the seismic reflection images alone. Low values of the acoustic impedance in the basement rocks were shown to be prospective by geochemical analysis of rock cores, as would be predicted for later gold mineralization in weak, decalcified rocks. 相似文献
5.
Gul Muhammet Ak M. Fatih 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(8):1231-1262
Stochastic Environmental Research and Risk Assessment - Occupational safety issues encountered in the worksite environment are the issues that companies should consider in improving their... 相似文献
6.
In this paper a fuzzy dynamic wave routing model (FDWRM) for unsteady flow simulation in open channels is presented. The continuity equation of the dynamic wave routing model is preserved in its original form while the momentum equation is replaced by a fuzzy rule based model which is developed on the principle that during unsteady flow the disturbances in the form of discontinuities in the gradient of the physical parameters will propagate along the characteristics with a velocity equal to that of velocity of the shallow water wave. The model gets rid off the assumptions associated with the momentum equation by replacing it with the fuzzy rule based model. It overcomes the necessity of calculating friction slope (Sf) in flow routing and hence the associated uncertainties are eliminated. The robustness of the fuzzy rule based model enables the FDWRM to march the solution even in regions where the aforementioned assumptions are violated. Also the model can be used for flow routing in curved channels. When the model is applied to hypothetical flood routing problems in a river it is observed that the results are comparable to those of an implicit numerical model (INM) which solves the dynamic wave equations using an implicit numerical scheme. The model is also applied to a real case of flow routing in a field canal. The results match well with the measured data and the model performs better than the INM. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
7.
为提高地下水定量遥感的评估精度,完善评估内容,扩大评估模型的适用范围,本文从水文地质的角度出发对地下水赋存空间、补给条件和地表指示进行研究,确定以地层岩性、断裂密度、地形坡度、地貌类型、汇流累积量、地表温度、土壤湿度作为地下水富集性评估的7个指标.选择具有代表性和典型性的丹东为研究区,利用ALOS、SPOT、TM和DEM数据对7个指标进行提取和解译,通过分析各指标对地下水富集性的影响特性,首次建立模糊隶属度函数对各指标进行模糊评判.利用层次分析法分别计算孔隙型地下水和裂隙型地下水各指标的权重,采用加权合成算法首次建立了地下水遥感模糊评估指数GRSFAI.研究区实地调查的钻井和泉眼数据表明:GRSFAI与孔隙水出水量的决定系数为0.82,与裂隙水出水量的决定系数为0.57.依据研究区GRSFAI的分布特点对地下水富集性进行评估分级,分级结果与实际情况一致,与地下水分布规律相符.分析认为:GRSFAI能准确反映地下水富集程度,评估结果可靠,具有良好的适用性和推广应用能力. 相似文献
8.
Abstract Rainfall-runoff models are used to describe the hydrological behaviour of a river catchment. Many different models exist to simulate the physical processes of the relationship between precipitation and runoff. Some of them are based on simple and easy-to-handle concepts, others on highly sophisticated physical and mathematical approaches that require extreme effort in data input and handling. Recently, mathematical methods using linguistic variables, rather than conventional numerical variables applied extensively in other disciplines, are encroaching in hydrological studies. Among these is the application of a fuzzy rule-based modelling. In this paper an attempt was made to develop fuzzy rule-based routines to simulate the different processes involved in the generation of runoff from precipitation. These routines were implemented within a conceptual, modular, and semi-distributed model-the HBV model. The investigation involved determining which modules of this model could be replaced by the new approach and the necessary input data were identified. A fuzzy rule-based routine was then developed for each of the modules selected, and application and validation of the model was done on a rainfall-runoff analysis of the Neckar River catchment, in southwest Germany. 相似文献
9.
Analysis of structural fuzzy random seismic response 总被引：2，自引：0，他引：2
Ａｎａｌｙｓｉｓｏｆｓｔｒｕｃｔｕｒａｌｆｕｚｚｙｒａｎｄｏｍｓｅｉｓｍｉｃｒｅｓｐｏｎｓｅ张跃，王光远ＹｕｅＺＨＡＮＧａｎｄＧｕａｎｇ－ＹｕａｎＷＡＮＧ１）（ＤｅｐａｒｔｍｅｎｔｏｆＣｉｖｉｌＥｎｇｉｎｅｅｒｉｎｇＴｓｉｎｇｈｕａＵｎｉｖｅｒｓｉｔ... 相似文献
10.
In practice, rainfall–runoff relationships are achieved through a simply defined runoff coefficient concept that is widely used in many engineering hydrological designs in urban and rural areas. The simplicity of the method, with the sole requirement of runoff coefficient assessment, is the main attractiveness, in addition to its successful prediction of average runoff rates for a given rainfall record. Unfortunately, in the classical regression approach of the rainfall–runoff relationship, internal variabilities are not taken into consideration explicitly. The runoff coefficient is considered a constant value, and it is used without distinction of antecedent conditions for the calculation of runoff from the rainfall record. In this paper, various other uncertainty embedded versions of the runoff coefficient, and hence rainfall–runoff formulation, are presented in terms of statistics, probability, perturbation and, finally, fuzzy system modelling. It is concluded that the fuzzy logic approach yields the least relative error among the various alternative runoff calculation methods; therefore, it is recommended for use in future studies. The application of various alternatives is presented for two monthly rainfall‐runoff records around Istanbul, Turkey. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
11.
Classified real-time flood forecasting by coupling fuzzy clustering and neural network 总被引：2，自引：0，他引：2
Minglei REN Bende WANG Qiuhua LIANG Guangtao FU 《国际泥沙研究》2010,25(2):134-148
This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed. 相似文献
12.
结构振动的模糊建模与模糊控制规则提取 总被引：10，自引：0，他引：10
模糊振动控制中存在的模糊控制规则的建立大都依赖于主观经验的现状。对此本文提出了一种通过对结构振动模糊建模来产生控制规则的方法。首先，通过对系统运动状态变量的模糊化，建立结构振动的模糊关系模型；其次通过对结构振动的模糊关系模型的分析，提取出模糊控制规则；最后，通过一个单自由度体系的数值仿真方法进行了验证。 相似文献
13.
14.
Hidemi M. Ito Akihiko Wakayama Mitsuyuki Hoshiba De-Yi Feng Ming-Zhou Lin 《地震学报(英文版)》1993,6(4):813-821
We have developed a workstation-assisted information processing system. The system has three major functions: information
retrieval from seismic data, detection of earthquake precursors, and graphical display of relevant results. Fuzziness is inevitably
involved in these functions, an adequate treatment of which is vital. The system accepts instructions given by a successive
choice of words in hierarchal structure, which is followed by a tune-up of the corresponding membership function. The degrees
of fuzziness in the outputs are recognized visually, for example, by coloring. This contrivance together with dynamic data
exchange among the above functions facilitates the operation of the system.
The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,15, 399–406, 1993.
This study is partly supported by a project “Fuzzy systems and their applications to human and natural sciences” of Science
and Technology Agency. 相似文献
15.
A fuzzy parameterized probabilistic analysis (FPPA) method was developed in this study to assess risks associated with environmental
pollution-control problems. FPPA integrated environmental transport modeling, fuzzy transformation, probabilistic risk assessment,
fuzzy risk quantification into a general risk assessment framework, and was capable of handling uncertainties expressed as
fuzzy-parameterized stochastic distributions. The proposed method was applied to two environmental pollution problems, with
one being about the point-source pollution in a river system with uncertain water quality parameters and the other being concerned
with groundwater contaminant plume from waste landfill site with poorly known contaminant physical properties. The study results
indicated that the complex uncertain features had significant impacts on modeling and risk-assessment outputs; the degree
of impacts of modeling parameters were highly dependent on the level of imprecision of these parameters. The results also
implied that FPPA was capable of addressing vagueness or imprecision associated with probabilistic risk evaluation, and help
generate risk outputs that could be elucidated under different possibilistic levels. The proposed method could be used by
environmental managers to evaluate trade-offs involving risks and costs, as well as identify management solutions that sufficiently
hedge against dual uncertainties. 相似文献
16.
This paper presents an optimal regulation programme, grey fuzzy stochastic dynamic programming (GFSDP), for reservoir operation. It is composed of a grey system, fuzzy theory and dynamic programming. The grey system represents data by covering the whole range without loss of generality, and the fuzzy arithmetic takes charge of the rules of reservoir operation. The GFSDP deals with the multipurpose decision‐making problem by fuzzy optimization theorem. The practicability and effectiveness of the proposed approach is tested on the operation of the Shiman reservoir in Taiwan. The current M5 operating rule curves of this reservoir also are evaluated. The simulation results demonstrate that this new approach, in comparison with the M5 rule curves, has superior performance with regard to the total water deficit and number of monthly deficits. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
17.
Applying active control systems to civil engineering structures subjected to dynamic loading has received increasing interest. This study proposes an active pulse control model, termed unsupervised fuzzy neural network structural active pulse controller (UFN‐SAP controller), for controlling civil engineering structures under dynamic loading. The proposed controller combines an unsupervised neural network classification (UNC) model, an unsupervised fuzzy neural network (UFN) reasoning model, and an active pulse control strategy. The UFN‐SAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces determined via the UFN model based on the clusters, classified through the UNC model, with their corresponding control forces. Herein, we assume that the effect of the pulses on structure is delayed until just before the next sampling time so that the control force can be calculated in time, and applied. The UFN‐SAP controller also averts the difficulty of obtaining system parameters for a real structure for the algorithm to allow active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, proving the feasibility of applying the adaptive unsupervised neural network with the fuzzy classification approach to control civil engineering structures under dynamic loading. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
18.
Assimilation of fuzzy data by the BME method 总被引：1，自引：1，他引：0
Modern spatiotemporal geostatistics provides a powerful framework for generation of predictive maps over a spatiotemporal domain by accounting for general knowledge to define a space of plausible events and then restricting this space of plausible events to be consistent with available site-specific knowledge. The Bayesian maximum entropy (BME) method is one of the most widely used modern geostatistics methods. BME results from assigning probabilities of plausible events based on general knowledge through information maximization and then applying operational Bayesian conditionalization that can explicitly assimilate stochastic representations of various uncertain (soft) data bases. The paper demonstrates that fuzzy data sets can be indirectly assimilated by BME through a two-step process: (a) reinterpretation of the fuzzy data as probabilistic through a generalized defuzzification procedure, and (b) efficient assimilation of the probabilistic results of generalized defuzzification by the BME method. A numerical demonstration involves site-specific probabilistic results obtained from the generalized defuzzification of a simulated fuzzy data set and general knowledge that includes the spatial mean trend and correlation structure models. The parameters of these models can be inferred from the hard data equivalent values of the probabilistic results. Accordingly, details of inference based on probabilistic soft data are also considered. 相似文献
19.
Taher Rajaee 《洁净——土壤、空气、水》2010,38(3):275-286
Simulation approaches employed in suspended sediment processes are important in the areas of water resources and environmental engineering. In the current study, neuro‐fuzzy (NF), a combination of wavelet transform and neuro‐fuzzy (WNF), multi linear regression (MLR), and the conventional sediment rating curve (SRC) models were considered for suspended sediment load (S) modeling in a gauging station in the USA. In the proposed WNF model, the discrete wavelet analysis was linked to a NF approach. To achieve this aim, the observed time series of river flow discharge (Q) and S were decomposed to sub time series at different scales by discrete wavelet transform. Afterwards, the effective sub time series were added together to obtain a useful Q and S time series for prediction. Eventually, the obtained total time series were imposed as inputs to the NF method for daily S prediction. The results illustrated that the predicted values by the proposed WNF model were in good agreement with the observed S values and gave better results than other models. Furthermore, the WNF model satisfactorily estimated the cumulative suspended sediment load and produced relatively reasonable predictions for extreme values of S, while NF, MLR, and SRC models provided unacceptable predictions. 相似文献
20.
Assessing traffic noise impact based on probabilistic and fuzzy approaches under uncertainty 总被引：1，自引：1，他引：0
Wei Peng Rene V. Mayorga 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(4):541-550
Traffic noise can cause severe sound pollution for human communities. This paper proposes a hybrid approach to assess traffic
noise impact under uncertainty. There are many factors influencing traffic noise level, but only three traffic parameters,
namely, traffic flow, traffic speed and traffic component, are highly uncertain. These uncertain parameters are represented
by probability distributions, and Monte Carlo simulations are performed to generate a noise distribution after considering
about other certain influencing factors. Fuzzy set and binary fuzzy relations as well as probability analysis method are applied
to identify the predicted traffic noise impacts in qualitatively and quantitatively. The applicability of this proposed technique
is demonstrated using a case study. 相似文献