• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.71-84
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• 2022

The objective of this study is to develop a two-dimensional (2D) flood model that can perform accurate flood analysis with simple input data. The 2D flood inundation models currently used to create flood forecast maps require complex input data and grid generation tools. This sometimes requires a lot of time and effort for flood modeling, and there may be difficulties in constructing input data depending on the situation. In order to compensate for these shortcomings, in this study, a grid-based model that can derive accurate and rapid flood analysis by reflecting correct topography as simple input data was developed. The calculation efficiency was improved by extending the existing 2×2 sub-grid model to a 5×5. In order to examine the accuracy and applicability of the model, it was applied to the Gamcheon Basin where both urban and river flooding occurred due to Typhoon Rusa. For efficient flood analysis according to user's selection, flood wave propagation patterns, accuracy and execution time according to grid size and number of sub-grids were investigated. The developed model is expected to be highly useful for flood disaster mapping as it can present the results of flooding analysis for various situations, from the flood inundation map showing accurate flooding to the flood risk map showing only approximate flooding.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.101-110
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• 2010

The increased occurrence of flooding due to typhoons and local rainfall has necessitated damage prevention through the systematic construction of damage history and quantitative analysis of flood prediction data. In this study, we constructed a disaster information map for practical use by combining digital images and continuous cadastral maps of damaged areas using a geographic information system to provide basic data and attribute information. In addition, we predicted the areas at risk of flash floods by calculating the flood capacity of the study area for different rainfall frequencies through flood inundation simulation, which was used to obtain comprehensive disaster information. Further, we calculated the extent of the flooded area and the damage rate for different rainfall frequencies using cadastral information. Flood inundation simulation in the case of heavy rainfall was found to help improve the ability to react to a flood and enhance the efficiency of rescue work by supporting decision-making for disaster management.

• Smart Structures and Systems
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• v.13 no.6
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• pp.1065-1094
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• 2014

Civil engineers always face the challenge of uncertainty in planning, building, and maintaining infrastructure. These works rely heavily on a variety of surveying and monitoring techniques. Unmanned aerial vehicles (UAVs) are an effective approach to obtain information from an additional view, and potentially bring significant benefits to civil engineering. This paper gives an overview of the state of UAV developments and their possible applications in civil engineering. The paper begins with an introduction to UAV hardware, software, and control methodologies. It also reviews the latest developments in technologies related to UAVs, such as control theories, navigation methods, and image processing. Finally, the paper concludes with a summary of the potential applications of UAV to seismic risk assessment, transportation, disaster response, construction management, surveying and mapping, and flood monitoring and assessment.

• Atmosphere
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• v.27 no.2
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• pp.189-197
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• 2017

Progressing from weather forecasts and warnings to multi-hazard impact-based forecast and warning services represents a paradigm shift in service delivery. Urban flooding is a typical meteorological disaster. This study proposes support plan for urban flooding impact-based forecast by providing inundation risk matrix. To achieve this goal, we first configured storm sewer management model (SWMM) to analyze 1D pipe networks and then grid based inundation analysis model (GIAM) to analyze 2D inundation depth over the Gangnam drainage area with $7.4km^2$. The accuracy of the simulated inundation results for heavy rainfall in 2010 and 2011 are 0.61 and 0.57 in POD index, respectively. 20 inundation scenarios responding on rainfall scenarios with 10~200 mm interval are produced for 60 and 120 minutes of rainfall duration. When the inundation damage thresholds are defined as pre-occurrence stage, occurrence stage to $0.01km^2$, 0.01 to $0.1km^2$, and $0.1km^2$ or more in area with a depth of 0.5 m or more, rainfall thresholds responding on each inundation damage threshold results in: 0 to 20 mm, 20 to 50 mm, 50 to 80 mm, and 80 mm or more in the rainfall duration 60 minutes and 0 to 30 mm, 30 to 70 mm, 70 to 110 mm, and 110 mm or more in the rainfall duration 120 minutes. Rainfall thresholds as a trigger of urban inundation damage can be used to form an inundation risk matrix. It is expected to be used for urban flood impact forecasting.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.93-104
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• 2020

Climate change has emerged as a social issue, and efforts have been made to overcome it in various ways. However, climate change due to global warming is likely to accelerate further than we expected, and the associated damage is also likely to increase. This tendency is expected to continue in the future, leading to difficulties for the existing water resource management system based on historical data. The precipitation in 2020 caused property damage and loss of life due to the extreme rainfall exceeding the design rainfall with the very unusual prolonged rainy days. With this 2020 flood, we should no longer be passive in managing flood risk due to uncertainties in climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.1 s.28
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• pp.69-77
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• 2004

Since there are a lot of changes in climate on domestic and natural disasters owing to the disturbance-development of the land, damages of properties and human life frequently occur due to the coastal floodings. Accordingly, it is necessary to find the area where the danger of flooding coasts is relatively high and to inform resident the characteristics of the area As a part of preventive land management to minimize the flooding damages of the coastal area, this study suggested the generation of the coastal flood hazard map that provides detailed information such as refuge path, a place of refuge, and the location of medical supplies, food, and main rescue equipment, etc. This study selected the southern region of Daebu-do as an exemplary area, conducted a document study to establish GIS data, secured pre-structured data, and suggested the method of establishing GIS data fit to the study area. In particular, it emphasized the efficient construction of the geographical spatial data that were accurate, economic, objective, and realistic in supporting the modeling to predict the flooding zone. The specific type of established database was divided into flooding risk area, flooding warning area, and flooding-volume area. The prototype of coastal flood hazard map can be widely used for efficient disaster management. Furthermore, it is considered that the map could be applied for arousing residents' attentions to the flooding, prior education, and local governments' management actions against the danger of flooding.

• Korean Journal of Community Nutrition
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• v.7 no.2
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• pp.219-231
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• 2002

The purpose of this study was to collect basic data on the prevention of and education about diabetes mellitus for the nutritional management of a diabetes mellitus risk group. The study which took place in Kangbukgu, Seoul, involved a diabetes mellitus risk group (DMR $\geq$ 110 mg/dL, 61), of males and females, aged 36 to 68 years, and a group of healthy people as a control group ( < 110 mg/dL, 183), using luting blood sugar (FBS) levels. The proportion of people in the abnormal range was higher in the DMR than that of control group for total cholesterol, high-density lipoproteins-cholesterol (HDL-C), total protein, glutamic oxaloacetic transaminase (GOT), glutamic pyruvic transaminase (GPT) and creatinine. Particularly with respect to serum protein the proportion in the DMR in abnormal range (p < 0.05) was significantly higher than that of the control group. The proportion in the DMR with a family history of disease was significantly higher than that of the control group (p < 0.01). Using body mass index (BMI), waist-hip ratio (WHR) and relative body weight (RBW), the obesity indices in the DMR was higher than that of the control group. Comparing the DMR and the control group with respect to dietary habits, it seems that the DMR had more undesirable dietary habits than the control group. When the intake of each nutrient for the DMR and the control group was compared to the Korean recommended dietary allowances (RDA), the proportion of excess intake and deficient intake in the DMR was higher than that of the control group. The DMR showed a greater undesirable dietary intake pattern as compared to that of the control group, based on the RDA. With respect to the dietary diversity score (DDS) and the mean adequacy ratio (MAR) for quality estimation of the overall flood intake, the DMR showed a feater undesirable pattern than the control group. According to the above results, the DMR tended to have more undesirable eating habits when compared to the control group. Therefore, to provide a more efficient nutritional education program for the DMR we must conduct lurker studies on eating habits, so as to provide systematic nutritional management based on theme differences between the DMR and the control group.