• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.310-313
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• 2012

Recently, Land reclamation created agricultural land which is farming. Agricultural land which is farming have accident with frequency it is damage to crop of from brine. So, desalinization is the first priority prerequisite task in using the in reclaimed farm land. Vibrant research and technical development is working for reclaimed of desaliaization. But, Current technology is impossible desalinization of reclaimed land. As fast almost of people don't worry about concentration of salt in using the land reclamation of agricultural land irrigation water and river mouth of fountainhead of efforting from freshwater lake also ebb and flow of the tide land reclamation of agricultural land influnce from an increase of salt concentration by weather conditions and a malfunction of sea dike sluice In this paper, current is increased salt concentration in real time graphs were implemented to utilize external servers in using the WCDMA module. Inaddition it have to operate alarm in increase of salt concentration. besides, this program have implemented realtime concentration of salt monitoring system which save date in realtime the user can check again.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.983-999
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• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.31-39
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• 2017

In order to examine if met-masts wind data can exchange each other for wind resource assessment, an investigation was carried out in Kimnyeong and Haengwon regions of Jeju Island. The two regions are both simple terrain and 4.31 km away from each other. The one-year wind speed data measured by 70 m-high anemometers of each met-mast of the two regions were analysed in detail. Measure-Correlate-Predict (MCP) method was applied to the two regions using the 10-year Automatic Weather System (AWS) wind data of Gujwa region for creating 10-year Wind Statistics by running WindPRO software. The two 10-year Wind Statistics were applied to the self-met mast point for self prediction of Annual Energy Production (AEP) and Capacity Factor (CF) and the each other's met mast point for mutual prediction of them. As a result, when self-prediction values were reference, relative errors of mutual prediction values were less than 1% for AEP and CF so that met masts wind data under the same condition of this study could exchange each other for estimating accurate wind resource.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.3
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• pp.190-198
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• 2023

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMY_normal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.2
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• pp.75-88
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• 2017

This study analyzed wind flows based on spatial and geomorphological characteristics of Daegu Metropolitan City. A three-stage analysis was performed, starting with a comparison of meteorological relationships between local wind direction (synoptic wind) and local wind flow. In the second stage the study area was subdivided into districts and suburban districts to analyze the relative change of local wind flow. In stage three, the formation of wind corridor for local wind flow, wind flow for the entire urban space, and spatial relationships between flows were verified comparatively using KLAM_21. Three results are notable, the first of which is a low correlation between synoptic wind of a region, and local wind, flow in terms of meteorology. Secondly, observations of local wind flow at five downtown districts and two suburban districts showed that there were diverse wind directions at each measurement point. This indicates that the spatial and geomorphological characteristics of areas neighboring the measurement points could affect the local wind flow. Thirdly, verifying the results analyzed using KLAM_21, compared to Atomatic Weather System(AWS) measurement data, confirmed the reliability of the numerical modelling analysis. It was determined that local wind flow in a city performs a spatial function and role in ameliorating the urban heat island phenomena. This indicates that, when an urban planning project is designed, the urban heat island phenomena could be ameliorated effectively and sustainably if local wind flow caused by immediate spatial and geomorphological characteristics is confirmed systematically and techniques are intentionally applied to connect the flows spatially within areas where urban heat islands occur.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.173-181
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• 2016

The objective of this study is to subdivide the tactical considerations (METT+TC; Mission, Enemy, Terrain & Weather, Troops available, Time available, Civil considerations) through Delphi method and prioritize those via AHP (Analytic Hierarchy Process). Though it has been taken for granted that the tactical considerations were inevitable for decision making relating to military operations, their segmentation and priority have not been studied sufficiently in military. The data for Delphi method and AHP were based on interview with military experts and questionnaires answered by those. Six tactical considerations were segmented into 34 sub-considerations by Delphi, and Six tactical considerations and 34 sub-ones were prioritized through AHP in attack and defense aspects. If the research results will be embedded into database of automatic command and control system (e.g. ACTIS; Army Tactical Command Information System), effective decision-making process will get easier and faster.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.739-745
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• 2018

Recently, the incidence of heavy rainfall is increasing. Therefore, a rainfall analysis should be performed considering increasing frequency. The current rainfall analysis for hydrologic design use the hourly rainfall data of ASOS with a density of 36 km on the Korean Peninsula. Therefore, medium and small scale watershed included Thiessen network at the same rainfall point are analyzed with the same design rainfall and time distribution. This causes problem that the watershed characteristics can not be considered. In addition, there is a problem that the temporal-spatial change of the heavy rainfall occurring in the range of 10~20 km can not be considered. In this study, Author estimated design rainfall considering heavy rainfall using minutely rainfall data of AWS, which are relatively dense than ASOS. Also, author analyzed the time distribution and runoff of each case to estimate the huff's method suitable for the watershed. The research result will contribute to the estimation of the design hydrologic data considering the heavy rainfall and watershed characteristics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.