• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.24-38
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• 2019

Due to the dramatic spatial changes caused by industrialization, environmental problems such as air pollution and urban heat island phenomenon, etc. are occurring in cities. In this case, the wind corridor, which is a passage through which fresh and cool air generated in forests outside cities move to the downtown, can be used as a spatial planning method for improving urban environmental problems. Cold air is determined by the characteristics of the flow depending on the topography and land use of cities, and based on this, the medium- and long-term plan should be established. Therefore, this study analyzed the flow of cold air at night through the KLAM_21 model in Suwon-si, Gyeonggi-do, to prepare the basic data required to apply the wind corridors. As a result, it turned out that cold air of Suwon-si was mainly generated from Gwanggyo Mountain that is a large mountain area in the north, and flowed into the urbanization promotion area, and about three hours after sunset, cold air flowed into the downtown. By district, the depth, wind speed, and direction of the cold air layer were formed differently according to the characteristics of the topography and land use. In the areas where large forests were adjacent, the flow of cold air was active. There are three main wind corridors where cold air flows to the downtown of Suwon-si, all of which are formed around rivers. Especially, if the connection between rivers and the surrounding green areas is high, the effect of wind corridors is found to be significant. In order to utilize the wind corridors of Suwon-si, based on the results of this study, it is necessary to make climate maps through actual survey and complex analysis of cold air flow and establish mid-to-long-term plans for the conservation and expansion of major wind corridors.

• Proceedings of The Korean Society of Health Promotion Conference
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• 2004.10a
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• pp.1-16
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• 2004

This paper considers the possibilities of health promotion from the following perspectives; (1) IUHPE, (2) socio-cultural similarities, (3) action research, and (4) learning from our past. 1. The IUHPE values decentralized activities through regions, and countries such as Japan, Korea, Hong Kong, Taiwan and China belong to NPWP region. Since IUHPE World Conference was held in Japan in 1995, Japan used to occupy more than 60% of NPWP membership. After 2001, membership is increasing rapidly in Chinese speaking sub-region. The transnational collaboration is still in its beginning phase. 2. Confucianism is one of key points. Confucian tradition should not be seen only as obstacles but as advantages to seek a form of health promotion more acceptable in East Asia. 3. Within the new public health framework, people are expected to create and live their health. However, especially in Japan, the tendency of 'lacking of face-to-face explicit interactions' is still common at health-promotion settings as well as academic settings. Therefore, the author tried participatory approaches such as asking WlFY (interactive questions designed for subjects to review their daily life and environment) and as introducing round table interactions. So far, majority of participants welcome new trials. 4. The following social phenomena are comparatively discussed after Japanese invasion and occupation of Korea ended in 1945; ·status of oriental medicine, ·separation of dispensary services, and ·health promotion specialist as a national license. In contrast to Japanese' tendency of maintaining the status quo and postponing of substantial social change, trend toward rapid and dynamic social changes are more commonly observed in Korea. Although all of above possibilities are still in their beginning stages, they are going to offer interesting directions waiting for further challenges and accompanying researches.

• Journal of Biosystems Engineering
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• v.16 no.4
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• pp.384-398
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• 1991

A computer program was developed to select the optimum size of farm machine and analyze its operation costs according to various farming conditions. It was written in FORTRAN 77 and BASIC languages and can be run on any personal computer having Korean Standard Complete Type and Korean Language Code. The program was developed as a user-friendly type so that users can carry out easily the costs analysis for the whole farm work or respective operation in rice production, and for plowing, rotarying and pest controlling in upland. The program can analyze simultaneously three different machines in plowing & rotarying and two machines in transplanting, pest controlling and harvesting operations. The input data are the sizes of arable lands, possible working days and number of laborers during the opimum working period, and custom rates varying depending on regions and individual farming conditions. We can find out the results such as the selected optimum combination farm machines, the overs and shorts of working days relative to the planned working period, capacities of the machines, break-even points by custom rate, fixed costs for a month, and utilization costs in a hectare.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.83-92
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• 2023

This study was established to investigate the site environment of mixed forests in Korea and to estimate the growth and yield of stands using national forest resources inventory data. The growth of mixed forests was derived by applying the Chapman-Richards model with diameter at breast height (DBH), height, and cross-sectional area at breast height (BA), and the yield of mixed forests was derived by applying stepwise regression analysis with factors such as cross-sectional area at breast height, site index (SI), age, and standing tree density per ha. Mixed forests were found to be growing in various locations. By climate zone, more than half of them were distributed in the temperate central region. By altitude, about 62% were distributed at 101-400 m. The fitness indexes (FI) for the growth model of mixed forests, which is the independent variable of stand age, were 0.32 for the DBH estimation, 0.22 for the height estimation, and 0.18 for the basal area at breast height estimation, which were somewhat low. However, considering the graph and residual between the estimated and measured values of the estimation equation, the use of this estimation model is not expected to cause any particular problems. The yield prediction model of mixed forests was derived as follows: Stand volume =-162.6859+6.3434 ∙ BA+9.9214 ∙ SI+0.7271 ∙ Age, which is a step- by-step input of basal area at breast height (BA), site index (SI), and age among several growth factors, and the determination coefficient (R²) of the equation was about 96%. Using our optimal growth and yield prediction model, a makeshift stand yield table was created. This table of mixed forests was also used to derive the rotation of the highest production in volume.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.