• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.627-641
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• 2012

We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.42.2-42.2
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• 2014

We investigate the history of formation of ~400 galaxies in four Abell clusters at 0.04

• Journal of Energy Engineering
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• v.27 no.4
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• pp.36-49
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• 2018

This study analyzed the public preference and acceptance regarding renewable energy projects through Choice Based Conjoint Analysis. The results show that the surveyed respondents consider the leading authority of the projects, as the most important factor when considering participating in renewable energy initiatives. Following this, the mode of participation and profit distribution and the power plant location are also viewed as important, whereas participation through decision making regarding the projects was less important. Also when participating in renewable energy projects, respondents tend to prefer to financially participating through loans or owning shares rather than volunteering support for the business such as sharing information, stating one's views, or providing cooperation and coordination. Therefore, the focus is on distributional justice, such as financial investment and profit distribution, rather than procedural justice, for instance decision making. When analyzing the part-worths utilities for the participation attribute, the respondents most preferred to receiving dividends based on earnings by owning shares with the local government in charge of the entire projects. As a consequence, the results suggest that it is important to have local government get involved and have trust-worthy governing systems in place for the initiation of the public participating-renewable energy projects.