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Donghyun Kim, Heechan Han, Wonjoon Wang and Hung Soo Kim
Accurate water level prediction is one of the important challenges in various fields such as hydrology, natural disasters, and water resources management studies. In this study, a deep neural network and a long short-term memory model were applied for wa...
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Kanghyeok Lee, Changhyun Choi, Do Hyoung Shin and Hung Soo Kim
Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was ...
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Jungwook Kim, Deokhwan Kim, Hongjun Joo, Huiseong Noh, Jongso Lee and Hung Soo Kim
The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall?runoff model. However, it does not focus on peak flow and on representative parameter for va...
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Narae Kang, Soojun Kim, Yonsoo Kim, Huiseong Noh, Seung Jin Hong and Hung Soo Kim
Recently, urban areas have experienced frequent, large-scale flooding, a situation that has been aggravated by climate change. This study aims to improve the urban drainage system to facilitate climate change adaptation. A methodology and a series of mit...
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Jaewon Kwak, Soojun Kim, Gilho Kim, Vijay P. Singh, Jungsool Park and Hung Soo Kim
Long-term streamflow data are vital for analysis of hydrological droughts. Using an artificial neural network (ANN) model and nine tree-ring indices, this study reconstructed the annual streamflow of the Sacramento River for the period from 1560 to 1871....
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Soojun Kim, Huiseong Noh, Jaewon Jung, Hwandon Jun and Hung Soo Kim
The impacts of two factors on future regional-scale runoff were assessed: the external factor of climate change and the internal factor of a recently completed large-scale water resources project. A rainfall-runoff model was built (using the Soil and Wat...
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Soojun Kim, Yonsoo Kim, Narae Kang and Hung Soo Kim
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Younghun Jung, Venkatesh Merwade, Soojun Kim, Narae Kang, Yonsoo Kim, Keonhaeng Lee, Gilho Kim and Hung Soo Kim
Generalized likelihood uncertainty estimation (GLUE) is one of the widely-used methods for quantifying uncertainty in flood inundation mapping. However, the subjective nature of its application involving the definition of the likelihood measure and the c...
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