• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.1-25
• /
• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.58 no.3
• /
• pp.57-69
• /
• 2016

In this study, we analyzed the extreme rainfall distribution scenarios based on probable rainfall calculation and applying various time distribution models over the landslide high risk zones in urban areas. We used observed rainfall data form total 71 ASOS (Automated Synoptic Observing System) station and AWS (Automatic Weather Station) in KMA (Korea Meteorological Administration), and we analyzed the linear trends for 1-hr and 24-hr annual maximum rainfall series using simple linear regression method, which are identified their increasing trends with slopes of 0.035 and 0.660 during 1961-2014, respectively. The Gumbel distribution was applied to obtain the return period and probability precipitation for each duration. The IDF (Intensity-Duration-Frequency) curves for landslide high risk zones were derived by applying integrated probability precipitation intensity equation. Results from IDF analysis indicate that the probability precipitation varies from 31.4~38.3 % for 1 hr duration, and 33.0~47.9 % for 24 hr duration. It also showed different results for each area. The $Huff-4^{th}$ Quartile method as well as Mononobe distribution were selected as the rainfall distribution scenarios of landslide high risk zones. The results of this study can be used to provide boundary conditions for slope collapse analysis, to analyze sediment disaster risk, and to use as input data for risk prediction of debris flow.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.3
• /
• pp.9-19
• /
• 2015

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

• Atmosphere
• /
• v.19 no.1
• /
• pp.9-36
• /
• 2009

Two heavy snowfall events occurred in Yeongnam and Yeongdong regions of the Korean Peninsula during the period from 4 to 6 March 2005 are analyzed. The events were developed by two different meso-scale snow clouds associated with an extratropical low passing over the Western Pacific. Based on synoptic data, GOES-9 satellite images, and precipitation amount data, the events were named as Sokcho and Busan cases, respectively. We analyzed the development mechanism of the events using meterological variables from the NCEP(National Centers for Environmental Prediction) /NCAR(National Centers for Atmospheric Research) reanalysis data such as potential vorticity(PV), divergence, tropopause undulation, static stability, and meridional wind circulation. The present analyses show that in the case of Sokcho, the cyclonic circulation in the lower atmosphere in the strong baroclinic region induced the cyclonic circulation in the upper atmosphere. The cyclonic circulation in the lower and upper atmosphere caused a heavy snowfall in the Sokcho region. In the case of Busan, the strong cyclonic circulation in the upper atmosphere was initiated by the stratospheric air intrusion with the high positive PV into the troposphere during the tropopause folding. The upper strong cyclonic circulation enhanced the cyclonic circulation in the lower disturbed atmosphere due to the extratropical low. This lower cyclonic circulation in turn, intensified the upper cyclonic circulation, that caused a heavy snowfall in the Busan region.

• 한국신재생에너지학회:학술대회논문집
• /
• 2010.06a
• /
• pp.185.2-185.2
• /
• 2010

한반도 국가바람지도(김현구, 2009)는 한국에너지기술연구원에서 지식경제부의 부처임무사업으로 구축되었으며 현재 웹서비스(http://www.kier-wind.org)를 통하여 정보를 제공하고 있다. 국가바람지도는 수치기상예측(NWP; Numerical Weather Prediction) 모델을 이용하여 영토, 영해에 대해 $1km{\times}1km$의 고해상도로 작성한 뒤(이순환 등, 2009) 풍력자원 정보로 재가공되었다. 한반도 국가바람지도는 5년의 장기간에 대한 시계열 수치기상예측에 의하여 구축되었기 때문에 데이터베이스(DB; database)의 효율적 관리가 필연적으로 요구된다. MM5 또는 WRF 모델의 고유 출력포맷의 자료구조는 풍력자원분석에 필요한 기상요소 외에도 대기과학자에게 필요한 수많은 기상인자를 종합적으로 포함하고 있다. 따라서 2차원 층(layer) 또는 3차원 공간분포 분석 및 계산격자인 셀(cell)에서의 1차원 시계열 분석 등 다양한 자료축출에는 비효율적인 자료구조가 된다. 이러한 자료구조의 불편을 해소하기 위해서는 기상요소별로 독립적이고 빈번한 시계열 자료 추출에 효율성을 가지며 어떤 프로그래밍 언어를 사용하든지 직관적으로 쉽게 사용할 수 있는 바람지도 데이터베이스의 재구성이 요구된다. 이에 대용량 수치자료의 처리 측면에서 장점을 가지는 과학기술 프로그래밍 언어인 IDL을 기반으로 국가바람지도의 자료구조를 효율화하여 데이터베이스화 하였으며 IDL에 내재된 그래픽 기능을 활용하여 가시화를 구현함으로써 연구개발자의 입장에서 국가바람지도의 활용성 및 효율성을 향상시키고자 하였다.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.4
• /
• pp.15-25
• /
• 2012

This study is to develop a web-based real-time agricultural flood management system(RAFMS) for 378 agricultural reservoirs equipped with auto water level gauge stations. The RAFMS was designed to operate linking with Rural Agricultural Water Resource Information System(RAWRIS) which supports data viz. real-time rainfall and water level necessary for RAFMS. The system was constituted to monitor the floods simultaneously at each reservoir by calculating the real-time reservoir inflow from watersheds, water level, and release to downstream. In addition, the system has the prediction function for the flood by applying weather forecasting data from Korea Meteorological Administration(KMA).

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.3
• /
• pp.28-37
• /
• 2020

This study developed a deep learning model that predicts rental demand for public bicycles. For this, public bicycle rental data, weather data, and subway usage data were collected. After building an exponential smoothing model, ARIMA model and LSTM-based deep learning model, forecasting errors were compared and evaluated using MSE and MAE evaluation indicators. Based on the analysis results, MSE 348.74 and MAE 14.15 were calculated using the exponential smoothing model. The ARIMA model produced MSE 170.10 and MAE 9.30 values. In addition, MSE 120.22 and MAE 6.76 values were calculated using the deep learning model. Compared to the value of the exponential smoothing model, the MSE of the ARIMA model decreased by 51% and the MAE by 34%. In addition, the MSE of the deep learning model decreased by 66% and the MAE by 52%, which was found to have the least error in the deep learning model. These results show that the prediction error in public bicycle rental demand forecasting can be greatly reduced by applying the deep learning model.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.617-620
• /
• 2017

The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.

• Journal of Korean Society of Disaster and Security
• /
• v.9 no.2
• /
• pp.23-32
• /
• 2016

Today the issue of deterioration of industrial complexes that are located close to life space of residents has been raised as a cause of threats to the safety of local communities. In this study, in order to improve the current risk analysis and scope of community notification, simulated threat zones were comparatively analyzed by utilizing the threat zones of alternative accident scenarios and modes of seasonal weather, and the area with a high probability of damage upon the leakage of toxic substances was predicted by examining wind directions observed at each time slot for each season. In addition, limit evacuation time and minimum separation distance to minimize casualties were suggested, and a proposal to enable more reasonable safety measures for on-site workers and nearby residents made by reviewing the risk management plan currently utilized for emergency response.

• Journal of Satellite, Information and Communications
• /
• v.12 no.3
• /
• pp.1-7
• /
• 2017

Climate change adaptation must be prepared, because the pattern of climate change in Korea is higher than the global average. In particular, it is estimated that Korea's economic loss due to climate change will reach 2,800 trillion won, and at least 300 trillion won will be needed for adaptation to climate change(KEI, 2011). Accurate climate change forecasts and impact forecasts are essential for efficient use of enormous climate change adaptation costs. For this climate change prediction and impact analysis, it is necessary to grasp not only the global average concentration but also the inhomogeneity of the greenhouse gas concentration which appears in each region. In this study, we analyze the feasibility of developing a greenhouse gas observation satellite, which is a cause of climate change, and present a development plan for a low orbit environmental satellite by examining the current status of the operation of the greenhouse gas observation satellite. The GHG monitoring satellite is expected to expand the scope of environmental monitoring by water/soil/ecology in addition to climate change, along with weather/agriculture/soil observation satellites.