• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Journal of Ecology and Environment
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• v.29 no.4
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• pp.389-398
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• 2006

To examine sediment characteristics and find anthropogenic effects on riverine wetland ecosystems, paleoecological study was carried out at Bam islands in Seoul. Three hundred cm deep sediment cores were retrieved and dated with the lamination analysis method until 36 cm depth (1986). Sediments were divided into three zones based on the depth profiles of physico-chemical variables: below 160 cm depth (before 1968), between 160 and 40cm depths and above 40cm depth (after 1986). Physico-chemical characteristics were very variable between 160 and 40cm depths and this indicates unstable sedimentation environment. Even though heavy metal concentrations were relatively low, Cd and As contents have increased continuously. Dry mass accumulation rates during $1968{\sim}1986\;and\;1987{\sim}2003$ were 140 and $21\;kg\;m^{-2}\;yr^{-1}$, respectively. This was related to flooding intensity and duration. Bulk density, water content, loss on ignition, N, C, C/N ratio were very similar to other river delta but Ca, Na and K contents were 2 to 4 times higher than others. Heavy metal contents except Pb were lower or similar to those in other studied marshes in Korea. Heavy metal and Mg contents were correlated with each other and this suggests that the source of heavy metals be parent rock. From $^{13}C$ dating dates of organic materials in sediment, it is suggested that organic matter originated from the watershed and flooding intensity in the watershed might be responsible for the source of sediments. This study provides reference data for the comparison of sediment characteristics at islands in river and for the management of Bam islands.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.1-7
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• 2023

This paper developed a training model to classify normal roads and flooded roads using artificial intelligence technology. We expanded the diversity of learning data using various data augmentation techniques and implemented a model that shows good performance in various environments. Transfer learning was performed using the CNN-based Resnet152v2 model as a pre-learning model. During the model learning process, the performance of the final model was improved through various parameter tuning and optimization processes. Learning was implemented in Python using Google Colab NVIDIA Tesla T4 GPU, and the test results showed that flooding situations were detected with very high accuracy in the test dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.313-323
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• 2019

Deep Tunnel system is a large-scale urban flood control facility installed underground in order to reinforce the lack of drainage systems in developed cities. In a structure like a deep tunnel system, the undular bore generated in the downstream causes a problem in the hydraulic stability of the tunnel. In this study, to investigate the influence of the undular bore on the hydraulic stability at the "Shinwol rainwater storage and drainage system", under construction for the first time in the country, a hydraulic model experiment was conducted on various flooding inflow scenarios. As a result of the hydraulic model experiment carried out in this study, the undular bore generated downstream is trapped in the pipe while moving to upstream, pushes the compressed air. It is judged that overflow occurred by choking the vertical drop shaft in the process when this compressed air is being exhaust through the upstream vertical drop shaft and blocking flood inflow. In addition, the analysis of velocity of undular bore shows that the undular bore transfers energy, and at this time, the pressure rose in the pipe and the velocity increment occurred of the undular bore. Further studies are needed to predict the size and velocity of undular bore, which plays an important role in the hydraulic stability of the tunnel in the deep tunnel system.

• Journal of the Korean Institute of Navigation
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• v.15 no.2
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• pp.87-96
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• 1991

The change of water level at Mokpo Harbour and its adjacent coastal area due to the construction of the Youngsan Estuary Barrage and the Third Land Reclamation Work of estuary barren had been roughly expected. Periodical floods, which occur 2 times per month, are also being observed at the low lying commercial areas near the Mokpo Old Harbor. Although it is said that the highest tidal current component among the tidal current records at the approaching channel to Mokpo Harbor is reduced to 6 kts, because of the esturary barrage, they do not give any precise statement or a deep analysis for the flooding and periodical water level change under certain environmental conditions. Moreover, they never tried the analysis of development plan considering the natural disaster such as typhoon or other extreme conditions. Thus, it is necessary to collect and analyze the data related to floodings, harbor oscillations, currents, and water quality , etc. because of the development considering the extreme condition. Thus, it is necessary to collect and analyze the data related to floodings, harbor oscillations, currents, and water quality, etc. because of the development considering the extreme condition and to evaluate the field observation and measurement, including the numerical model simulation based on the scientific approaches. This study deals the problem of the water level change among the integrated analyses of the coastal area changes. The result can be used for the integrated planning to give a strong foundation and it will contribute to the development of local area.

• International Journal of Contents
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• v.13 no.4
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• pp.23-28
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• 2017

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.194-194
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• 2021

최근 지구온난화 및 이상기후 현상으로 인하여 집중호우의 빈도와 강도가 급증하고 있다. 그리고 급격한 도시화로 불투수 면적이 증가하여 도시지역에 침수피해가 빈번하게 발생하고 있는 실정이다. 이러한 침수피해를 방지하기 위하여 침수위험지구, 재해위험지구를 선정하여 집중호우에 대하여 집중관리를 하고 있지만 위험지구이외의 곳에서 침수가 발생할 경우 신속하게 대처하지 못하는 문제가 발생하고 있다. 또한, 하천이 범람하여 발생하는 외수침수의 경우 수위를 실시간으로 확인할 수 있어 미리 대응이 가능하지만, 내수침수의 경우 지하에 매설되어 있는 관로의 상태를 확인할 수 없기 때문에 순간적으로 발생하는 침수에 대하여 신속하게 대처를 해야 한다. 현재 침수 피해를 신속하게 대처하기 위하여 CCTV를 활용해 침수의 발생여부를 모니터링 하고 있지만 CCTV설치 지역에 비하여 적은 인력으로 모든 CCTV를 확인하지 못하여 침수피해를 신속하게 대처하지 못하고 있는 실정이다. 본 연구에서는 침수사진 자료를 CNN(Convolutional Neural Network)기법을 이용하여 학습시켜 침수의 발생여부를 판단하는 모델을 제안하였다. 딥러닝 기법의 CNN은 이미지의 특징을 추출하여 학습하는 과정을 가지게 되는데 학습이 완료된 모델은 침수사진의 특징을 파악하여 침수가 발생하였는지에 대한 여부를 자동적으로 판단하게 된다. 본 연구결과를 CCTV관재센터 혹은 지자체와의 연계를 통하여 침수의 발생여부를 자동적으로 판단해주는 시스템이 개발된다면 신속한 침수피해 대처가 이루어 질 수 있을 것이라 판단된다.