• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.73-82
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• 2013

The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.236-236
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• 2015

Flood is one of the major natural disasters affecting millions of people. Thailand also, frequently faces with this type of disaster. Especially, 2011 mega flood in Central Thailand, inundated highway severely attributed to the failure of national economic and risk to life. Lesson learned from such an extreme event caused flood monitoring and warning becomes one of the sound mitigations. The highway flood hazard mapping accomplished in this research is one of the strategies. This is due to highway flood is the potential risk to life and limb, and potential damage to property. Monitoring and warning therefore help reducing live and property losses. In this study, degree of highway flood hazard was assessed by weighting factors for each cause of the highway flood using Multi Criteria Analysis (MCA) based Analytic Hierarchy Process (AHP). These weighting factors are the essential information to classify the degree of highway flood hazard to enable pinpoint on flood monitoring and flood warning in hazard areas. The highway flood causes were then investigated. It was found that three major factors influence to the highway flood are namely the highway characteristics, the hydrological characteristics and the land topography characteristics. The weight of importance for each cause of the highway flood in the whole country was assessed by weighting 3 major factors influence to the highway flood. According to the result of MCA analysis, the highway, the hydrological and the land topography characteristics were respectively weighted as 35, 35 and 30 percent influence to the cause of highway flood. These weighting factors were further utilized to classify the degree of highway flood hazard. The Weight Linear Combination (WLC) method was used to compute the total score of all highways according to each factor. This score was later used to categorize highway flood as high, moderate and low degree of hazard levels. Highway flood hazard map accomplished in this research study is applicable to serve as the handy tool for highway flood warning. However, to complete the whole warning process, flood water level monitoring system for example the camera gauge should be installed in the hazard highway. This is expected to serve as a simple flood monitor as part of the warning system during such extreme or critical event.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.549-557
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• 2013

An flood inundation map is able to convey spatial distribution of inundation to a decision maker for flood risk management. A roughness coefficient with unclear values and a discharge obtained from the stage-discharge rating equation are key sources of uncertainty in flood inundation mapping by using a hydraulic model. Also, the uncertainty analysis needs an observation for the flood inundation, and satellite images is useful to obtain spatial distribution of flood. Accordingly, the objective of this study is to quantify uncertainty arising roughness and discharge in flood inundation mapping by using a hydraulic model and a satellite image. To perform this, flood inundations were simulated by HEC-RAS and terrain analysis, and ISODATA (Iterative Self-Organizing Data Analysis) was used to classify waterbody from Landsat 5TM imagery. The classified waterbody was used as an observation to calculate F-statistic (likelihood measure) in GLUE (Generalized Likelihood Uncertainty Estimation). The results from GLUE show that flood inundation areas are 74.59 $km^2$ for lower 5 % uncertainty bound and 151.95 $km^2$ for upper 95% uncertainty bound, respectively. The quantification of uncertainty in flood inundation mapping will play a significant role in realizing the efficient flood risk management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2293-2302
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• 2013

Flood inundation map has been used as a fundamental information in flood risk management. However, there are various sources of uncertainty in flood inundation mapping, which can be another risk in preventing damage from flood. Therefore, it is necessary to remove or reduce uncertainty sources to improve the accuracy of flood inundation maps. However, the entire removal of uncertainty source may be impossible and inefficient due to limitations of knowledge and finance. Sensitivity analysis of uncertainty sources allows an efficient flood risk management by considering various conditions in flood inundation mapping because an uncertainty source under different conditions may propagate in different ways. The objectives of this study are (1) to perform sensitivity analysis of uncertainty sources by different conditions on flood inundation map using the FOA method and (2) to find a major contributor to a propagated uncertainty in the flood inundation map in Flatrock at Columbus, U.S.A. Result of this study illustrates that an uncertainty in a variable is differently propagated to flood inundation map by combination with other uncertainty sources. Moreover, elevation error was found to be the most sensitive to uncertainty in the flood inundation map of the study reach.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.128-133
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• 1998

To assess the flood damages and to provide necessary information for preventing future catastrophe, it is necessary to appraise the inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in southern part of Korea. JERS L-band SAR data obtained during the summer of 1997 were used to delineate the inundated areas. In addition, Landsat TM data were also used for analyzing the land cover condition before the flooding. Once the two data sets were co-registered, each data was separately classified. The water surface areas extracted from the SAR data and the land cover map generated using the TM data were overlaid to determine the flood inundated areas. Although manual interpretation of water surfaces from the SAR image seems rather simple, the computer classification of water body requires clear understanding of radar backscattering behavior on the earth's surfaces. It was found that some surface features, such as rice fields, runaway, and tidal flat, have very similar radar backscatter to water surface. Even though satellite SAR data have a great advantage over optical remote sensor data for obtaining imagery on time and would provide valuable information to analyze flood, it should be cautious to separate the exact areas of flood inundation from the similar features.

• Journal of Environmental Science International
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• v.13 no.1
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• pp.19-26
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• 2004

The purpose of the study was to construct a forecast system of flood inundation area at natural stream channels. The study built the system to interpret the flood inundation area in four stages ; constructing topography data around the stream channel, interpreting flood discharge, interpreting flood elevation in the stream channel, and interpreting the flood inundation and mapping. According to the result of the analysis, as for the characteristic of flood inundation around the area within the purview of this study, although there were areas where flood inundation over a bank caused a flooded area, the failure of the internal drainage in the ground lower than flood elevation caused more serious problems. Rather than the existing method where only the estimated flood elevation data is used based on the hydrographical stream channel trace model(such as the HEC-RAS model) to establish the flood inundation area, if the procedure introduced in this study was applied to interpret the floodplain, actual flood inundation area could be visibly confirmed.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.52-61
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• 2004

Korea Water Resources Corporation carried out LiDAR survey to construct detailed terrain data for flood mapping and it is expected that much money is required in flood mapping of all over the country. Therefore, it is desirable to use NGIS digital map to construct preliminary modelling data for selection of flood mapping area. And the analysis of DEM error with respect to scale of digital map is necessary for the sake of applying digital map as the input data of flood mapping. We compared DEM from digital map with DEM from LiDAR survey. Especially we analyzed DEM error characteristics that is occurred with respect to the interpolation method that is used to construct DEM from TIN of digital map. As a result of analysis, digital map(1:1,000) showed smaller error than digital map(1:5,000) and DEM applying linear interpolation showed smaller error than DEM applying quintic interpolation. Especially, variation of DEM error by cell resolution was evaluated as very slight because urban district was composed of gentle slope.

• Journal of Environmental Science International
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• v.25 no.8
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• pp.1165-1176
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• 2016

The accuracy of digital elevation models (DEMs) is crucial for properly estimating flood inundation area. DEM pixel size is especially important when generating flood inundation maps of small streams with a channel width of less than 50 m. In Korea, DEMs with large spatial resolutions of 30 m have been widely applied to generate flood inundation maps, even for small streams. Additionally, when making river master plans, field observations of stream cross-sections, as well as reference points in the middle of the river, have not previously been used to enhance the DEM. In this study, it was graphically demonstrated that high-resolution DEMs can increase the accuracy of flood inundation mapping, especially for small streams. Also, a methodology was proposed to modify the existing low-resolution DEMs by adding additional survey reference points, including river cross-sections, and interpolating them into a high spatial resolution DEM using the inverse distance weighting method. For verification purposes, the modified DEM was applied to Han stream on Jeju Island. The modified DEM showed much better accuracy when describing morphological features near the stream. Moreover, the flood inundation maps were formulated with the original 30 m pixel DEM and the modified 0.1 m pixel DEM using HEC-RAS modeling of the actual flood event of Typhoon Nari, and then compared with the flood history map of Nari. The results clearly indicated that the modified DEM generated a similar inundation area, but a very poor estimate of inundation area was derived from the original low-resolution DEM.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.787-799
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• 2018

Accurate and effective mapping is critical step to monitor the spatial distribution and change of flood inundated area in large scale flood event. In this study, we try to suggest methods to use low spatial resolution satellite optical imagery for flood mapping, which has high temporal resolution to cover wide geographical area several times per a day. We selected the Sebou watershed flood in Morocco that was occurred in early 2010, in which several hundred $km^2$ area of the Gharb lowland plain was inundated. MODIS daily surface reflectance product was used to detect the flooded area. The study area showed several distinct spectral patterns within the flooded area, which included pure turbid water and turbid water with vegetation. The flooded area was extracted by thresholding on selected band reflectance and water-related spectral indices. Accuracy of these flooding detection methods were assessed by the reference map obtained from Landsat-5 TM image and qualitative interpretation of the flood map derived. Over 90% of accuracies were obtained for three methods except for the NDWI threshold. Two spectral bands of SWIR and red were essential to detect the flooded area and the simple thresholding on these bands was effective to detect the flooded area. NIR band did not play important role to detect the flooded area while it was useful to separate the water-vegetation mixed flooded classes from the purely water surface.