• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.53-62
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• 2013

Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.208-220
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• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.3
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• pp.360-369
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• 2012

Parent material, climate, topography, biological factors, and time are considered five soil forming factors. This study was conducted to elucidate the effects of several environment factors on soil distribution using quantitative analysis method, called soil series estimation algorithm in the soils of Jeju Island. We selected environment factors including mean temperature, annual precipitation, surface geology, altitude, slope, aspect, altitude difference within 1 $km^2$ area, topographic wetness index, distance from the shore, distance from the mountain peak, and landuse for a quantitative analysis. We analyzed the ranges of environment factors for each soil series and calculated probabilities of possible-soil series for certain locations using estimation algorithm. The algorithm can predicted exact soil series on the soil map with correctness of 33% on $1^{st}$ ranking, 62% within $2^{nd}$ ranking, 74% within $5^{th}$ ranking after estimating using randomly extracted environment factors. In predicted soil map, soil sequences of Entisols-Alfisols-Andisols on northern area and Alfisols-Ultisols-Andisols on western area can be suggested along increasing altitude. More modeling studies will be needed for the genesis process of soils in Jeju Island.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.86-102
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• 2012

For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, and maps of the topography, geology, landcover and green infrastructure were constructed for a spatial database. The factors that influence flooded areas occurrence, such as slope gradient, slope, aspect and curvature of topography and distance from darinage, were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. The frequency ratio coefficient is overlaid for flooded areas susceptibility mapping as each factor's ratings. Then the flooded areas susceptibility map was verified and compared using the existing flooded areas. As the verification results, the frequency ratio model showed 82% in prediction accuracy. The method can be used to reduce hazards associated with flooded areas and to plan land use.

• Journal of Environmental Health Sciences
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• v.46 no.4
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• pp.376-387
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• 2020

Objectives: This study is aimed at offering basic data for making plans for offensive odor management after researching offensive odor occurrence and characteristics in Gangwon Province. Methods: The data used in the study is based on offensive odor data analyzed by the Gangwon Institute of Health and Environment from 2012 to 2019. The data were reclassified by year, month, facility, and region to identify characteristics of occurrence. Finally, a distribution map of offensive odors was created using ArcGIS. Results: The highest monthly frequency of offensive odor occurrence falls in June, August, and July, and the summer season and third quarter are the highest. According to the latest eight-year data for Gangwon Province, complaints about offensive odors in county areas are more frequent than those in city areas. There are many offensive odor complaints in Wonju, Cheorwon, and Heongsung. The main offensive odor emission facilities are livestock and waste treatment (recycling) facilities. Complaints about offensive odors are relatively lower the Yeongdong area than Yeongseo area, which is considered to be the result of characteristics of land-sea breezes and geographical factors. Offensive odors from livestock facilities count for an average of 53.9% of the total, and the inadequacy rate of livestock facilities averages 36.9%. Conclusions: To maintain a clean environment in Gangwon Province, it is strongly recommended that an offensive odor reduction plan for livestock facilities be established. Areas with a high density of offensive odor occurrence should be identified and systematically managed with short- and mid-term measures. If offensive odors is managed using GIS, it is possible to identify the characteristics of occurrence by time and space and also by facility. In addition, since systematic data management is possible, it is believed that a rapid response to offensive odors, prediction of their spread, and efficient management are possible.

• Journal of Soil and Groundwater Environment
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• v.10 no.5
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• pp.52-60
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• 2005

The conceptual model of under-sampled study area will include a great amount of uncertainty. In this study, we investigate the applicability of Markov chain model in a spatial domain as a tool for minimizing the uncertainty arose from the lack of data. A new formulation is developed to generalize the previous two-dimensional coupled Markov chain model, which has more versatility to fit any computational sequence. Furthermore, the computational algorithm is improved to utilize more conditioning information and reduce the artifacts, such as the artificial parcel inclination, caused by sequential computation. A generalized 20 coupled Markov chain (GCMC) is tested through applying a hypothetical soil map to evaluate the appropriateness as a substituting model for conventional geostatistical models. Comparing to sequential indicator model (SIS), the simulation results from GCMC shows lower entropy at the boundaries of indicators which is closer to real soil maps. For under-sampled indicators, however, GCMC under-estimates the presence of the indicators, which is a common aspect of all other geostatistical models. To improve this under-estimation, further study on data fusion (or assimilation) inclusion in the GCMC is required.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.1-11
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• 2007

H.264/AVC uses variable block sizes to achieve significant coding gain. It has 7 different coding modes having different motion compensation block sizes in Inter slice, and 2 different intra prediction modes in Intra slice. This fine-tuned new coding feature has achieved far more significant coding gain compared with previous video coding standards. However, extremely high computational complexity is required when rate-distortion optimization (RDO) algorithm is used. This computational complexity is a major problem in implementing real-time H.264/AVC encoder on computationally constrained devices. Therefore, there is a clear need for complexity reduction algorithm of H.264/AVC such as fast mode decision. In this paper, we propose a fast mode decision with early $P8\times8$ mode rejection based on block size activity using large block history map (LBHM). Simulation results show that without any meaningful degradation, the proposed method reduces whole encoding time on average by 53%. Also the hybrid usage of the proposed method and the early SKIP mode decision in H.264/AVC reference model reduces whole encoding time by 63% on average.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.5
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• pp.506-513
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• 2020

Due to climate change, there is an increasing risk of complex (hybrid) disasters, comprising rising sea-levels, typhoons, and torrential rains. This study focuses on Marine City, Busan, a new residential city built on a former landfill site in Suyeong Bay, which recently suffered massive flood damage following a combination of typhoons, storm surges, and wave overtopping and run-up. Preparations for similar complex disasters in future will depend on risk impact assessment and prioritization to establish appropriate countermeasures. A framework was first developed for this study, followed by the collection of data on flood prediction and socioeconomic risk factors. Five socioeconomic risk factors were identified: (1) population density, (2) basement accommodation, (3) building density and design, (4) design of sidewalks, and (5) design of roads. For each factor, absolute criteria were determined with which to assess their level of risk, while expert surveys were consulted to weight each factor. The results were classified into four levels and the risk level was calculated according to the sea-level rise predictions for the year 2100 and a 100-year return period for storm surge and rainfall: Attention 43 %, Caution 24 %, Alert 21 %, and Danger 11 %. Finally, each level, indicated by a different color, was depicted on a complex disaster risk map.