• The Korean Journal of Applied Statistics
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• v.37 no.6
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• pp.801-815
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• 2024

Future uncertainty means that future results or phenomena cannot be accurately predicted. Since deterministic population projection based on such future uncertainty has clear limitations, so many advanced population research institutes and international organizations have emphasized the importance of probabilistic population prediction. It also presents probabilistic predictions in the research areas of climate, process, precipitation, and weather. However, the KOSTAT and various organizations in korea are only in scenario-based deterministic population projection, and only the need for probabilistic population prediction is raised. Therefore, this paper points out that when future uncertainties exist, the limitations and problems of decisive population projection should be examined, and the future population should be examined with probabilistic population prediction, and the results are presented. As a result of the analysis, in terms of the probabilistic confidence interval (5th quartile, 95th quartile), 5,106 to 51.2 million people in 2025, 5,053 to 5,082 million in 2030, 4,829 to 4,8 million in 2040, 4,425 to 45,5 million in 2050, and the last forecast, in 2062, the number below 40 million, is expected to be 37.33 to 33.3 million, and the rapid population deceleration over 33 years was the biggest factor rapidly decline in the fertility rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1651-1664
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• 2013

Precise analysis on deterioration processes of road pavements is not so simple matter due to severe uncertainty originated from a lot of explanatory variables engaged in. For those reasons, most analytical models for pavement deterioration prediction have often preferred to probabilistic approaches than deterministic models. However, the general probabilistic approaches that treat overall characteristics of population or entire sample would not be suitable for providing detail or localized information on their changing process. Considering the aspects, this paper aimed to suggest a stochastic disaggregation method to analyze the localized deterioration speeds and its variances changed by time and condition states. In addition, life expectancies and their uncertainty were estimated by probabilistic algorithm using the disaggregated stochastic process. For an empirical study, pavement inspection data (crack) accumulated from 2003 to 2010 from Korean national highway network was applied. This study can contribute to securing reliability of life cycle cost analysis, which is one of the primary analyses in road asset management, with much advanced deterioration forecasting functions. In addition, it would be meaningful trials as fundamental research for preventive maintenance strategy that demands essential understanding on changing process of the deterioration speed of pavement.

• Smart Structures and Systems
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• v.15 no.3
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.879-889
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• 2014

In this study, to estimate loss of life considered flood characteristics using the relationship derived from analysis of historical dam break cases and the factors determining loss of life, the loss of life module applying in LIFESim and loss of life estimation by means of a mortality function were suggested and applicability for domestic dam watershed was examined. The flood characteristics, such as water depth, flow velocity and arrival time were simulated by FLDWAV model and flood risk area were predicted by using inundation depth. Based on this, the effects of warning, evacuation and shelter were considered to estimate the number of people exposed to the flood. In order to estimate fatality rates based on the exposed population, flood hazard zone is assigned to three different zones. Then, total fatality numbers were predicted after determining lethality or mortality function for each zone. In the future, the prediction of loss of life due to dam break floods will quantitatively evaluate flood risk and employ to establish flood mitigation measures at downstream applying probabilistic flood scenarios.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.657-668
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• 2022

With the occurrence of localized heavy rain while river flow has increased, both flow and rainfall cause riverside flood damages. As the degree of damage varies according to the level of social and economic impact, it is required to secure sufficient forecast lead time for flood response in areas with high population and asset density. In this study, the author established a flood risk matrix using ensemble rainfall runoff modeling and evaluated its applicability in order to increase the damage reduction effect by securing the time required for flood response. The flood risk matrix constructs the flood damage impact level (X-axis) using flood damage data and predicts the likelihood of flood occurrence (Y-axis) according to the result of ensemble rainfall runoff modeling using LENS rainfall data and as well as probabilistic forecasting. Therefore, the author introduced a method for determining the impact level of flood damage using historical flood damage data and quantitative flood damage assessment methods. It was compared with the existing flood warning data and the damage situation at the flood warning points in the Taehwa River Basin and the Hyeongsan River Basin in the Nakdong River Region. As a result, the analysis showed that it was possible to predict the time and degree of flood risk from up to three days in advance. Hence, it will be helpful for damage reduction activities by securing the lead time for flood response.