• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.129-137
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• 2020

In performing the structural analysis, the foundation is considered to be a fixed end as a plastic hinge model. In this study, the displacements of the foundation, pier, and shoe were compared when the foundation modeled as a fixed end, a shallow foundation constructed on bedrock of 2m depth, and a pile foundation constructed in the 10m to 20m depth of bedrock. The shear force was also compared, and the probability of damage was calculated and compared for the critical condition. When calculated as a fixed end, the displacement of the foundation converged to 0mm, but the shallow foundation built on the bedrock with a depth of 2m caused relatively displacement, and the pile foundation constructed to contact the bedrock with a depth of 18m caused a larger displacement. In addition, it was analyzed that the displacement of the foundation, which is the lower structure, affects the displacement of the super structure, but the difference in shear force applied to the foundation was insignificant in the three cases. There was no difference between the shallow foundation and the pile foundation in the influence on the displacement of the top of the pier, but there was a big difference from the analysis assuming as a fixed end.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.837-844
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• 2023

Small and medium-sized mountain rivers that flow through steep, confined valleys carry large amounts of coarse-grained sediment and woody debris during floods. It causes an increase in flood water level by aggrading the riverbed and the cross-section blockage due to driftwood accumulation during flooding. However, the existing flood level calculation in the river basic plan does not consider these changes. In this study, using the Typhoon Hinnamnor flood in September 2022 as an example, we performed numerical simulations using the HEC-RAS model, taking into account the blockage of a cross-section at the bridge and changes in riverbed elevation that occurred during floods, and analyzed the flood level to predict flood risk. This study's results show that flooding occurs if more than 30% of the cross-section is blocked. The rise of flood water levels corresponds to that of the riverbed due to sediment deposition. These results can be used as basic data to prevent and effectively manage flood damage and contribute to establishing flood defense measures that consider actual phenomena.