• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.453-462
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• 2008

The objective of this paper is to compare the prediction performances of different landslide hazard maps based on topographic data stemming from different sources of elevation. The geostatistical framework of kriging, which can properly integrate spatial data with different accuracy, is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. A case study from Boeun, Korea illustrates that the integration of elevation and slope maps derived from different data yielded different prediction performances for landslide hazard mapping. The landslide hazard map constructed by using the elevation and the associated slope maps based on geostatistical integration of spot heights and ASTER-based elevation resulted in the best prediction performance. Landslide hazard mapping using elevation and slope maps derived from the interpolation of only sparse spot heights showed the worst prediction performance.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.161-166
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• 2012

This Study, as part of a study on the application plan of overseas noise prediction models suitable for making domestic noise maps, analyzed the correlation between the differences in predicted noise levels by individual noise prediction model and surveyed data on General roads and Expressways. Separation distances of 5m and 10m, respectively were set from the ends of the general roads and the expressways at the points of measurements and to check the distribution patterns of sound power levels, the levels were measured at the heights of 1.5m and 3m, respectively. The latest revised versions of the five models (CRTN, RLS90, NMPB, Nord2000, ASJ2008) suggested in The Method of making Noise Maps were used as prediction models, and predicted noise levels were calculated by using commercial software SoundPLAN (Ver 7.1).

• Atmosphere
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• v.31 no.1
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• pp.1-15
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• 2021

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-mean-square error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.251-267
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• 2002

Self Organizing Maps(SOM) by Kohonen is very fast algorithm in neural networks. But it doesn't show sure rules of training results. In this paper, we introduce to Bayesian Learning for Self Organizing Maps(BLSOM) which combines self organizing maps with Bayesian learning. So it supports explanatory power of models and improves prediction. BLSOM has global optima anywhere but SOM has not. This is proved by experiment in this paper.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.387-402
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• 2005

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.123-127
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• 2002

In this paper, we propose spatial prediction model for landslide hazard mapping that can account for the fuzziness of boundaries in thematic maps showing the different environmental impacts, depending on the scales and the resolutions of them. The fuzziness or uncertainty of boundary is represented in favourability function based on fuzzy object concept and the effects of them are quantitatively evaluated with the help of cross validation procedures. To illustrate the proposed schemes, a case study from Boeun, Korea was carried out. As a result, the proposed schemes are helpful to account for intrinsic uncertainties in categorical maps and can be effectively adopted in spatial prediction models for other purposes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.295-298
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• 2004

Automobile door-handle is assembled with three parts that are base, skin and cover. Over-molding processing makes assembly of the base and skin. The skin part that was made by PVC polymer has various thickness. Plastic injection molding simulation of part including significant changed thickness as skin is an inaccuracy comparing with real injection molding. To solve this problem, two commercial flow prediction software that are Moldflow MPI and MAPS 3D were used in this study. Simulations were conducted for three types mesh. Taguchi method was applied for simulation experiments. It will be need to compare with simulation results and real over-molding behavior in the near future.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.75.1-75.1
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• 2019

We introduce a novel method for reconstructing the projected dark matter mass maps of merging galaxy clusters by applying the convolutional neural network (CNN) to their weak lensing maps. We generate synthesized grayscale images from given weak lensing maps that preserve their averaged galaxy ellipticity. We then apply them to multi-layered CNN with architectures of alternating convolution and trans-convolution filters to predict the mass maps. We train our architecture with 1,000 Subaru/Suprime-Cam mock weak lensing maps, and our method have better mass map prediction than the Kaiser-Squires method with the following three aspects: (1) better pixel-to-pixel correlation, (2) more accurate finding of density peak position, and (3) free from mass-sheet degeneracy. We also apply our method to the HST weak lensing map of the El Gordo cluster and compare our result to the previous studies.