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Natural Hazards - In this study, we present a novel methodology that may be used to analyze tsunami risk along coastal regions. The application of the proposed methodology is demonstrated for the... 相似文献
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Probabilistic-fuzzy health risk modeling 总被引：3，自引：2，他引：1
E.?Kentel M. M.?AralEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2004,18(5):324-338
Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods. 相似文献
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2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment 总被引：15，自引：4，他引：11
E. Kentel M. M. Aral 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(1):86-96
Risk estimates can be calculated using crisp estimates of the exposure variables (i.e., contaminant concentration, contact rate, exposure frequency and duration, body weight, and averaging time). However, aggregate and cumulative exposure studies require a better understanding of exposure variables and uncertainty and variability associated with them. Probabilistic risk assessment (PRA) studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. Two-dimensional Monte Carlo Analysis (2D MCA) is one of the advanced modeling approaches that may be used to conduct PRA studies. In this analysis the variables of the risk equation along with the parameters of these variables (for example mean and standard deviation for a normal distribution) are described in terms of probability density functions (PDFs). A variable described in this way is called a second order random variable. Significant data or considerable insight to uncertainty associated with these variables is necessary to develop the appropriate PDFs for these random parameters. Typically, available data and accuracy and reliability of such data are not sufficient for conducting a reliable 2D MCA. Thus, other theories and computational methods that propagate uncertainty and variability in exposure and health risk assessment are needed. One such theory is possibility analysis based on fuzzy set theory, which allows the utilization of incomplete information (incomplete information includes vague and imprecise information that is not sufficient to generate probability distributions for the parameters of the random variables of the risk equation) together with expert judgment. In this paper, as an alternative to 2D MCA, we are proposing a 2D Fuzzy Monte Carlo Analysis (2D FMCA) to overcome this difficulty. In this approach, instead of describing the parameters of PDFs used in defining the variables of the risk equation as random variables, we describe them as fuzzy numbers. This approach introduces new concepts and risk characterization methods. In this paper we provide a comparison of these two approaches relative to their computational requirements, data requirements and availability. For a hypothetical case, we also provide a comperative interpretation of the results generated. 相似文献
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Elcin Kentel Mustafa M. Aral 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(4):405-417
In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the
simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and
health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis,
to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment,
risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled
and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information
in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since
possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability
of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this
paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve
defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the
risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability
of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated
with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and
the results are discussed comparatively. 相似文献
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Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates 总被引：1，自引：0，他引：1
Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results. 相似文献
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