• Journal of Digital Convergence
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• v.14 no.8
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• pp.245-252
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• 2016

The purpose of this study is to make up for missing of weather informations from ASOS and AWS using artificial neural networks. We collected temperature, relative humidity and wind velocity for August during 5-yr (2011-2015) and sample designed artificial neural networks, assuming the Seoul weather station was missing. The result of sensitivity study on number of epoch shows that early stopping appeared at 2,000 epochs. Correlation between observation and prediction was higher than 0.6, especially temperature and humidity was higher than 0.9, 0.8 respectively. RMSE decreased gradually and training time increased exponentially with respect to increase of number of epochs. The predictability at 40 epoch was more than 80% effect on of improved results by the time the early stopping. It is expected to make it possible to use more detailed weather information via the rapid missing complemented by quick learning time within 2 seconds.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.561-568
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• 2020

Wave measurements using X-band radar have many advantages compared to other wave gauges including wave-rider buoy, P-u-v gauge and Acoustic Doppler Current Profiler (ADCP), etc.. For example, radar system has no risk of loss/damage in bad weather conditions, low maintenance cost, and provides spatial distribution of waves from deep to shallow water. This paper presents new methods for estimating significant wave heights of X-band marine radar images using Artificial Neural Network (ANN). We compared the time series of estimated significant wave heights (Hs) using various estimation methods, such as signal-to-noise ratio (${\sqrt{SNR}}$), both and ${\sqrt{SNR}}$ the peak period (TP), and ANN with 3 parameters (${\sqrt{SNR}}$, TP, and Rval > k). The estimated significant wave heights of the X-band images were compared with wave measurement using ADCP(AWC: Acoustic Wave and Current Profiler) at Hujeong Beach, Uljin, Korea. Estimation of Hs using ANN with 3 parameters (${\sqrt{SNR}}$, TP, and Rval > k) yields best result.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.34-43
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• 2009

Based on previous research on ways of reducing an urban heat island phenomenon via an introduction of wind corridors, I conducted this study to see what influence a change in land cover arising of or going through urbanization has on wind corridors of urban space. As a target place, I chose Daegu city where is a representative extreme heat place in Korea and has been also largely expanded in size by incorporating its neighboring areas since the 1980s, expecially Dalseo District whose surface temperature gap is large. The population of Dalseo District has been sharply increased since its creation as a new administrative district in 1988. I studied on the urban microclimate change for a 20-year period by using satellite images on summer months in 1987, 1997 and 2007 in time frames. The finding of this study found that a reduction of natural land cover and an increase of artificial land cover serves as a disadvantageous factor for cold air creation and flowing and strikingly lowers the amount and height of cold air in the downtown area. It seemed that the cold air creation and flowing functions are influenced by land cover. In order to steadily create cold air and secure its flowing, it is thought that urban development or urban regeneration should be implemented by analysing the characteristics of the space surrounding the city. By doing so, a pleasant and healthy city could be formed.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.312-320
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• 2020

Various LIDs with natural water circulation function are applied to reduce urban environmental problems and environmental impact of development projects. However, excessive Infiltration and evaporation of LID facilities dry the LID internal soil, thus reducing plant and microbial activity and reducing environmental re duction ability. The purpose of this study was to develop a real-time measurement system with complex sensors to derive the management plan of LID facilities. The test of measurable sensors and Internet of Things (IoT) application was conducted in artificial wetlands shaped in acrylic boxes. The applied sensors were intended to be built at a low cost considering the distributed LID and were based on Arduino and Raspberry Pi, which are relatively inexpensive and commercialized. In addition, the goal was to develop complex sensor measurements to analyze the current state o f LID facilities and the effects of maintenance and abnormal weather conditions. Sensors are required to measure wind direction, wind speed, rainfall, carbon dioxide, Micro-dust, temperature and humidity, acidity, and location information in real time. Data collection devices, storage server programs, and operation programs for PC and mobile devices were developed to collect, transmit and check the results of measured data from applied sensors. The measurements obtained through each sensor are passed through the Wifi module to the management server and stored on the database server in real time. Analysis of the four-month measurement result values conducted in this study confirmed the stability and applicability of ICT technology application to LID facilities. Real-time measured values are found to be able to utilize big data to evaluate the functions of LID facilities and derive maintenance measures.

• Korean Journal of Environment and Ecology
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• v.25 no.5
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• pp.717-724
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• 2011

The objectives of this study were to analyze environment in the forest and growth characteristics for investigating the characteristics of Robinia pseudoacacia distribution in a Pinus thunbergii forest at the coast. As a result of analyzing inhibitory factors of Robinia pseudoacacia distribution in a Pinus thunbergii forest at the coast, it is considered that the salt level included in a sea wind is supposed to be the primary factor of the slow growth for Robinia pseudoacacia since brown leaves, wilting and early leaf fall have appeared in the 0m spot from the artificial dune which has the high salt level. However, the soil properties and light environment hardly have a effect on the growth of Robinia pseudoacacia because there is no difference among planting places. Also, the growth ring of the horizontal root in 2year individuals 0.1~0.2m away from the dune have been formed for 1 year only as a consequence of analyzing growth rings of Robinia pseudoacacia growing on the coast. It can be infered that the nourishment of the horizontal root from individuals growing on the coast have been provided for the first 1 year only. It is estimated that, in case of the nearby areas on the coast, it is not enough to provided nourishment to the horizontal root due to obstructing the growth of new individuals by a sea wind, so the growth of the horizontal root would be hampered. Therefore, it is considered that impedient Robinia pseudoacacia distribution in a Pinus thunbergii forest at the coast is caused by making no growth of new horizontal roots and newborn individuals.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.533-544
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• 2015

Unlike the concrete structure built on land, that exposed to the marine environment is greatly degraded in durability due to the exposure to not only the physical action caused by sea wind, tide, and wave, but also the harsh conditions, including the chemical erosion and freeze-thaw which result from $SO_4{^{2-}}$, $Cl^-$ and $Mg^{2+}$ ions in seawater. In the process of the large scaled construction of submerged concrete structures, of course environmental hazardous substance, such as alkaline (pH) and heavy metals, may be leached. Thus, this issue needs to be adequately reviewed and studied. Therefore, this study attempted to develop a CSA (Calcium Sulfo Aluminate) activator using electric arc furnace reducing slags, as well as the eco-friendly concrete for artificial reefs using electric arc furnace oxidizing slag as aggregate for concrete. The strength properties of the eco-friendly concrete exposed to the marine environment were lower than those of the normal concrete by curing 28 days. This suggest that additional studies are needed to improve the early strength of the eco-friendly concrete. With respect to seawater resistance of the eco-friendly concrete, the average strength loss against 1 year of curing days reached 8-9%. the eco-friendly concrete using high volume of ground granulated blast furnace slags and high specific gravity of electronic arc furnace oxidizing slag demonstrated the sufficient usability as a freeze-thaw resistant material. With respect to heavy metal leaching properties of the eco-friendly concrete, heavy metal substances were immobilized by chemical bonding in the curing process through the hydration of concrete. Thus, heavy metal substances were neither identified at or below environmental hazard criteria nor detected, suggesting that the eco-friendly concrete is safe in terms of leaching of hazardous substances.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.2 no.4
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• pp.54-63
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• 1999

This study was conducted to suggest the ecological restoration and environmentally friendly revegetation technology for the rock-exposed cut-slopes by the Natural Topsoil Restoration Methods (NTRM) with the following restoration objectives; (1) prevention or reduction of wind and water erosion, (2) provision of food and cover for variety of animal species, (3) improvement of the visual or aesthetic quality of the disturbed slopes. On Nov. in 1995, the 5cm thick layer of artificial soil and 2cm thick layer of straw-mulching was attached at rock-exposed cut-slopes by NTRM without using anchor wire and anchor pin. The main results during four years surveying on the ground-coverage effect, plant growth, species diversity and importance values were summarized as follows. 1. Artificial soil attached at rock exposed cut-slopes was not eroded until recovered by tree and herbaceous vegetation in spite of not using anchor wire and anchor pin. Also it shows low soil hardiness and has almost the same amount of bacteria and fungi with in surrounding natural topsoil. 2. In 'combination for the woody vegetation', Lespedeza cyrtobotrya, Albizzia julibrissin, Rhus chinensis, Indigofera pseudo-tinctoria occupied upper layer vegetation. Since three years after seeding, Indigofera pseudo-tinctoria had overwhelmed the other woody plants and cool season foreign grasses, Erigeron canadensis, Taraxacum mongolicum, Commelina communis, Arundinella hirta (Thunberg) and Oenothera erythrosepala grows at lower part of the vegetation, 3. The heights of the Rhus chinensis grows 1.8m, Indigofera pseudo-tinctoria 2.0m, so it seems that the objectives of woody vegetation with native plants could be accomplished. 4. After 4 years later after seeding in 'combination for the herbaceous vegetation', the most dominant plant was Indigofera pseudo-tinctoria, the next was in order of cool-season grasses, Taraxacum mongolicum, Erigeron canadensis, lxeris dentata (Thunberg), Oenothera erythrosepala, Arundinella hirta (Thunberg). The diversity index in 'combination for woody vegetation' was higher than that in 'combination for the herbaceous vegetation'. The tendency of the intrusion of secondary succession plants was more effective in 'combination for the herbaceous vegetation' than in 'combination for the woody vegetation'.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of Conservation Science
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• v.35 no.2
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• pp.99-115
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• 2019

The Royal Tombs at Songsanri is one of the most important historic site for ancient historical study in Korean Peninsula. Since the excavation of the tombs, continuous exposure to the outside environment and the negative effects of the artificial air conditioning system have caused significant threats to the thermal environment stability of the tombs. Unlike the outside temperature that shows significant differences according to seasonal changes, the burial chamber of the tombs had a relatively stable temperature range of 11.4 to $22.2^{\circ}C$ throughout the year, and the standard deviation of temperature was within 3.5. It was revealed that major factors affecting the microclimate of the tombs were inflow of outdoor air, wind direction and speed, and all of them had closely related to airtightness of the tombs. The solar radiation was in inverse proportion to the thickness of burial mounds, and thus Royal Tomb of King Muryeong, which has the thickest burial mound, was least affected by solar radiation. Especially, microclimate of the tombs caused to the highest influence with artificial environmental changes due to access by people, which varied in proportion to the number of accessed people and time of stay. Currently, the inside of the tombs are sealed and always in saturated condition, it is very vulnerable to dew condensation. As a result of analyzing the possibility of condensation in each tomb, all the tomb No. 5, tomb No. 6 and Royal Tomb of King Muryeong had condensation most of the time throughout the year. It is required to make a proper conservation environment for the Royal Tombs at Songsanri.