• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.327-335
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• 2020

The method of selecting an existing flood hazard area via a numerical model requires considerable time and effort. In this regard, this study proposes a method for selecting flood vulnerable areas through topographic analysis based on a surface runoff mechanism to reduce the time and effort required. Flood vulnerable areas based on runoff mechanisms refer to those areas that are advantageous in terms of the flow accumulation characteristics of rainfall-runoff water at the surface, and they generally include lowlands, mild slopes, and rivers. For the analysis, a digital topographic map of the target area (Seoul) was employed. In addition, in the topographic analysis, eight topographic factors were considered, namely, the elevation, slope, profile and plan curvature, topographic wetness index (TWI), stream power index, and the distances from rivers and manholes. Moreover, receiver operating characteristic analysis was conducted between the topographic factors and actual inundation trace data. The results revealed that four topographic factors, namely, elevation, slope, TWI, and distance from manholes, explained the flooded area well. Thus, when a flood vulnerable area is selected, the prioritization method for various factors as proposed in this study can simplify the topographical analytical factors that contribute to flooding.

• 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.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.94-104
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• 2021

Atmospheric corrosion severity associated with aircraft parking environment was studied using metallic specimens, and temperature and humidity sensors installed at each aircraft operating base. Data were analyzed after a year of exposure. Silver was used to measure chloride deposition by integrating X-ray photoelectron spectroscopy depth profiles. Carbon steel was utilized to determine the corrosion rate by measuring the weight loss. The time of wetness was determined using temperature and humidity sensor data. Analysis of variance followed by Tukey's "honestly significant difference" test indicated that atmospheric environment inside the shelter varied significantly from that of unsheltered parking environment. The corrosion rate of unsheltered area also varies with the roof. Hierarchical clustering analysis of the measured data was used to classify air bases into groups with similar atmospheric corrosion. Bases where aircraft park at a shelter can be grouped together regardless of geographical location. Unsheltered bases located inland can also be grouped together with sheltered bases as long as the aircraft are parked under the roof. Environmental severity index was estimated using collected data and validated using the measured corrosion rate.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.927-932
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• 2010

This study made a nationwide metropolitan region map on the basis of disaster vulnerability and administrative boundary, and based on it, it divided small-sized regions and constructed disaster history of each region. For the disaster vulnerability, the study wrote slope, aspect, curvature, wetness index, and drainage density, compared and analyzed regions with disaster and geomorphic elements to distinct the factor with high correlations, and based on it, it divided small-sized regions for forecasting and warning system of middle regions(Gangwon province, Chungchung province, and Jeolla province). Through the method, Gangwon region were divided into 4 small-sized regions, Chungchung into 5 small-sized regions, and Jeolla into 6 small-sized regions.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.6
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• pp.37-45
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• 2005

Almost every year, Korea has been suffered from serious damages of lives and properties, due to landslides that are triggered by heavy rains in monsoon season. In this paper, we systematized the physically based landslide prediction model which consisted of 3 parts, infinite slope stability analysis model, groundwater flow model and soil depth model. To evaluate its applicability to the prediction of landslides, the data of actual landslides were plotted on the predicted areas on the GIS map. The matching rate of this model to the actual data was $84.8\%$. And the relation between hydrological and land form factors and potential landslide were analyzed.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Korean Journal of Ecology and Environment
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• v.49 no.3
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• pp.236-243
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• 2016

It was a case study to use as a basic data for efficient the preservation and management of subalpine coniferous forest in national parks. It is based on inhabitation condition of 210 individuals of Abies koreana Wilson that was found through local investigation in the sub-alpine zone of Jirisan National Park and Songnisan National Park. It analyzed the effect of the geographical location and topographical features, which are the basics of location environment, on the growth of A. koreana. The variables related to the growth of A. koreana are tree height and diameter at breast height. Topographical features include geographical longitude, altitude above sea level, slope of the mountains, aspect that describes the direction in which a slope faces and topographical wetness index. Topographical features were extracted through GIS spatial analysis. It used canonical correlation analysis to estimate whether the two variables groups have related to each other and how much they are related, if any, and estimated the effect of the geographical and topographical features on the growth structure of A. koreana using multiple regression analysis. The tree height and diameter at breast height that represent the growth structure of A. koreana show greater relation to geographical latitude distribution than topographical feature and the geographical and topographical factors show greater relation to diameter at breast height than tree height. The growth structure's variable and geographical and topographical variable of A. koreana have meaningful relation and the result shows that geographical and topographical variables explain 18.1% of the growth structure. The variables that affect the diameter at breast height of A. koreana are geographical latitude, topographical wetness index, aspect and altitude, which are put in order of statistical significance. The higher the latitude is, the smaller the diameter at breast height. Depending on the topographical feature, it becomes bigger. The variable that affects the tree height is topographical wetness index, which was the only meaningful variable. Overall, the tree height and diameter at breast height that are related to the growth structure of A. koreana are affected by geographical and topographical feature. It showed that the geographical feature affected it the most. Especially the effect of water among the topographical features is expected to be bigger than the other topographical factors. Based on the result, it is expected that geographical and topographical feature is an important factor for the growth structure of A. koreana. Even though it considered only the geographical and topographical features and used spatial analysis data produced by GIS, the research results will be useful for investigating and researching the growth environment of coniferous forest inhabiting in sub-alpine zone of national parks and are expected to be used as basic data for establishing measures to efficiently manage and preserve evergreen needleaf tree such as A. koreana.

• The Journal of Engineering Geology
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• v.22 no.1
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• pp.83-94
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• 2012

Slopes are commonly formed both above and below roads located in mountainous terrain and along riversides. The Buk-sil site, a cut slope formed below the road, collapsed in October, 2010. A field investigation determined the causes of failure as improper drainage of valley water from the slope above the road and direct seepage of road-surface water. These factors may have accelerated the collapse via complex interaction between water and sub-surface structures such as bedding. Projection analysis of the site showed the possible involvement of plane, wedge, and toppling failure. Safety factors calculated by Limit Equilibrium Analysis for plane and wedge failure were below the standard for wet conditions. The wetness index, analyzed using topographic factors of the study area, was 9.0-10.5, which is high compared with the values calculated for nearby areas. This finding indicates a high concentration of water flow. We consider that water-flow control on the upper road is crucial for enhancing slope stability at the Buk-sil site.

• The Journal of Engineering Geology
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• v.22 no.3
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• pp.283-291
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• 2012

Various studies regarding the prediction of landslides are underway internationally. Research into disaster prevention with regard to debris flows is a particular focus of research because this type of landslide can cause enormous damage over a short period. The objective of this study is to determine the hazard susceptibility of debris flow via predictions of surface water concentrations based on the concept that a debris flow is similar to a surface water flow, as it is influenced by mountain topography. This study considered urban areas affected by large debris flows or landslides. Digital mapping (including the slope and upslope contributing areas) and the wetness index were used to determine the relevant topographic factors and the hydrology of the area. We determined the hazard susceptibility of debris flow by predicting the surface water concentration based on the topography of the surrounding mountainous terrain. Results obtained using the distinct element method were used to derive a correlation equation between the weight and the impact force of the debris flow. We consider that in using a correlation equation, this method could assist in the effective installation of debris-flow-prevention structures.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.