Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).
The uncertainty assessment is important to improve the reliability of emission inventory data. The DARS (Data Attribute Rating System) have recommended as the uncertainty assessment technic of emission inventory by U.S. EPA (Environmental Protection Agency) EIIP (Emission Inventory Improvement Program). The DARS score is based on the perceived quality of the emission factor and activity data. Scores are assigned to four attributes; measurement/method, source specificity, spatial congruity and temporal congruity. The resulting emission factor and activity rate scores are combined to arrive at an overall confidence rating for the inventory. So DARS is believed to be a useful tool and may provide more information about inventories than the usual qualitative grading procedures (e.g. A through E). In this study, the uncertainty assessment for 2009 CAPSS (Clean Air Policy Support System) emission inventory is conducted by DARS. According to the result of this uncertainty assessment, the uncertainty for fugitive dust emission data is higher than other sources, the uncertainty of emission factor for surface coating is the highest value, and the uncertainty of activity data for motor cycle is the highest value. Also it is analysed that the improvement of uncertainty for activity data is as much important as the improvement for emission factor to upgrade the reliability of CAPSS emission inventory.
Uncertainties generated from the individual measured variables have an influence on the uncertainty of the experimental result through a data reduction equation. In this study, a performance test of a single stage axial type turbine is conducted, and total-to-total efficiencies are measured at the various off-design points In the low pressure and cold state. Based on an experimental apparatus, a data reduction equation for turbine efficiency is formulated and six measured variables are selected. Codes are written to calculate the efficiency, the uncertainty of the efficiency, and the sensitivity of the efficiency uncertainty by each of the measured quantities. The influence of each measured variable on the experimental result is figured out. Results show that the largest uncertainty magnification factor (UMF) value is obtained by the inlet total pressure among the six measured variables, and its value is always greater than one. The UMF values of the inlet total temperature, the torque, and the RPM are always one. The uncertainty percentage contribution (UPC) of the RPM shows th, lowest influence on the uncertainty of the turbine efficiency, but the UPC of the torque has the largest influence to the result among the measured variables. These results are applied to find the correct direction for meeting an uncertainty requirement of the experimental result in the planning or development Phase of experiment, and also to offer ideas for preparing a measurement system in the planning phase.
Purpose: The purposes of this study were to identify the level of total symptom distress, uncertainty, depression and ways of coping in women with endometriosis based on Mishel's model of Uncertainty in Chronic illness, and to exam the relationships among symptom distress, depression and ways of coping and the mediating effect of ways of coping between uncertainty and depression. Method: The research was used for correlational research design and data were collected with 123 women with endometriosis who live in a local area by convenience sampling. Results: The finding showed that the levels of symptom distress and uncertainty were moderate and the depression was above the middle level. There were positive relationships among symptom distress, uncertainty and depression but a negative relationship between problem focused coping and depression. The significant predictors for depression were symptom distress, uncertainty, and problem focused coping with 40% of explained variance. Problem focused coping showed mediating effect between uncertainty and depression. Conclusion: Therefore, nursing intervention for the strategy of increasing problem focused coping as well as lowering uncertainty and depression is recommended. Further study is needed to conduct a repetitive study with randomized nationwide population and to evaluate the theory with different outcomes for adaptation versus maladaptation.
Purpose: The purpose of this study was to explore factors affecting uncertainty and general well-being based on Uncertainty in Illness Theory. Methods: Data were collected from 125 outpatients who had received hemodialysis. The path model among four concepts, such as period of hemodialysis, social support, uncertainty, and general well-being, was tested. Tangible support, positive social interaction, affectionate, and emotional/informational support were measured as social support. Adaptation in the model was operationalized as general well-being which consisted of anxiety, depression, positive well-being, self-control, and general health. Results: All paths were statistically significant at the level of ${\alpha}$=.05. The significant paths were the path from period of hemodialysis to uncertainty (t=-2.86), social support to uncertainty (t=-2.01), uncertainty to general wellbeing (t=-2.85), and social support to general well-being (t=3.55). Conclusion: Patients who perceived low uncertainty and high social support were likely to feel well-being. Therefore, nurses should give patients appropriate information according to their needs and have meaningful interaction with patients to reduce their uncertainty and render social support.
In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.
Jeong, Hyesun;Lee, Yesul;Park, Jin Sup;Lee, Yoonju
대한간호학회지
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제54권2호
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pp.162-177
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2024
Purpose: This study aimed to investigate the influence of uncertainty-related factors on the health behavior of individuals with coronary artery disease (CAD) based on Mishel's uncertainty in illness theory (UIT). Methods: We conducted a cross-sectional study and path analysis to investigate uncertainty and factors related to health behavior. The study participants were 228 CAD patients who visited the outpatient cardiology department between September 2020 and June 2021. We used SPSS 25.0 and AMOS 25.0 software to analyze the data. Results: The final model demonstrated a good fit with the data. Eleven of the twelve paths were significant. Uncertainty positively affected danger and negatively affected self-efficacy and opportunity. Danger had a positive effect on perceived risk. Opportunity positively affected social support, self-efficacy, perceived benefit and intention, whereas it negatively affected perceived risk. Social support, self-efficacy, perceived benefit and intention had a positive effect on health behavior. We found that perceived benefit and intention had the most significant direct effects, whereas self-efficacy indirectly affected the relationship between uncertainty and health behavior. Conclusion: The path model is suitable for predicting the health behavior of CAD patients who experience uncertainty. When patients experience uncertainty, interventions to increase their self-efficacy are required first. Additionally, we need to develop programs that quickly shift to appraisal uncertainty as an opportunity, increase perceived benefits of health behavior, and improve intentions.
With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.
Computational Structural Engineering : An International Journal
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제1권2호
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pp.81-87
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2001
A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.
Heat rate is a representative index to estimate the performance of turbine cycle in nuclear power plant. Accuracy of heat rate calculation is dependent on the accuracy of measurement for plant status variables. Uncertainty of heat rate can be modeled using uncertainty propagation model. We developed practical estimation model of heat rate uncertainty using the propagation and regression model. The uncertainty model is used in the performance analysis system developed for the operating nuclear power plant.
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