• Journal of Navigation and Port Research
• /
• v.43 no.1
• /
• pp.64-71
• /
• 2019

It is very important to understand the mechanism of estuary topographic changes for the study of estuary management and treatment methods. In this study, the effects from the land-side, such as rainfall, river discharge, sediment discharge, and sea side, such as tide, tidal current, wave and surface sediments related to the topographic changes of the Nakdong river estuary were investigated and analyzed. Based on the analyzed data, topographic modeling was performed to analyze the topographic change and contribution of external force conditions. As a result of numerical modeling, the topographic change showed that erosion that predominates in the water directly affected by the discharge of the estuary barrage. The deposition predominates in the indirectly affected tideland. As sediments moved along the water way being sorted and distributed by the wave, the deposition predominated in the front of the barrier island. Compared with the deposition dominance, which is the result of the topographic change prediction, the impact of each external force condition gives larger erosion. However, the combined impact of each external force condition showed deposition dominant. Therefore, the topographic changes of the Nakdong river estuary are considered to be the result of various complex external factors. The impacts of each external force condition show the different contribution to each comparison area. These results should be considered when establishing the estuary management method. It must be understood that this is the result of complex interactions.

• The Journal of the Korea Contents Association
• /
• v.21 no.6
• /
• pp.35-43
• /
• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.43-45
• /
• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.99-109
• /
• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Journal of Forest and Environmental Science
• /
• v.14 no.1
• /
• pp.145-156
• /
• 1998

On the basis of data obtained from 5 forest roads(Backyang, Byongatae, Saorang, Bukyu and Dangrim forest road) collapsed under a heavy rainfall in Chunchon, Kangwondo, this study was carried out to predict the cutting slope failure of forest road by using Quantification theory(II). The results were summarized as follows; The cutting slope failure was chiefly occurred by correlated action of road structure, vegetation and topographical factors. The cutting slope failure predicted by partial correlation coefficients and range values was characterized by longer than 8m of cutting slope length, depper than 2.5m of soil depth, between $30^{\circ}$ and $50^{\circ}$ of original ground slope gradient, absence of vegetation coverage on cutting slope, and greater than $60^{\circ}$ of cutting slope gradient. And the rate of correct discrimination by analysis of cutting slope failure was 90.1%.

• 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).

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.1
• /
• pp.128-141
• /
• 2010

Drought indices such as SPI(Standard Precipitation Index) and PDSI(Palmer Drought Severity Index) estimated using ground observations are not enough to describe detail spatial distribution of drought condition. In this study, the drought index with improved spatial resolution was estimated by using the CART algorithm and ancillary data such as MODIS NDVI, MODIS LST, land cover, rainfall, average air temperature, SPI, and PDSI data. Estimated drought index using the proposed approach for the year 2008 demonstrates better spatial information than that of traditional approaches. Results show that the availability of satellite imageries and various associated data allows us to get improved spatial drought information using a data mining technique and ancillary data and get better understanding of drought condition and prediction.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.816-820
• /
• 2005

본 연구에서는 해안 도시 하천의 범람으로 인한 홍수 재해 발생시 예상될 수 있는 피해에 대해 적절한 홍수예경보 및 피난대책을 수립하고자 대표적인 해안 도시 하천의 특성을 가지는 부산시 온천천 유역을 대상으로 수치지도에서 각종 지형자료를 추출하였고 수문 GIS 자료를 구축하였다. 그리고, 하천 수리 분석을 위한 한계유출량 산정을 위해 HEC-RAS 모형을 이용 조위의 영향을 고려하여 홍수위 및 한계유출량을 산정하였고 수문 분석을 위한 도시 돌발 홍수 기준 우량 산정을 위해 PCSWMM 2002를 이용하여 기준 우량을 산정하였다. 전형적인 해안 도시 지역 유역 특성을 나타내는 부산시 온천천 유역에 대한 경보발령 기준을 설정하기 위하여 선정지점 세 곳의 한계수심 $H_{c1},\;H_{c2},\;H_{c3},\;H_{c4}$가 발생할 수 있는 강우량(위험 홍수량을 유발하는 위험 강우량(Trigger Rainfall))을 산정하였고 PCSWMM을 이용한 모형화 기법으로 해안 도시 돌발 홍수 기준 우량을 산정하였다. 산정 결과 온천천 유역의 홍수예경보 시스템과 이에 따른 홍수예경보 발령흐름도, 운영체계가 결정되어 해안 도시 돌발 홍수예경보 방안이 구축되었다. 해안 도시의 홍수 관리는 도시 우수 시스템, 하천, 해안 특성이 복합된 문제이다. 현재 해안 도시 지역의 홍수예경보 시스템 구축 실적이 전무한 실정임을 볼 때 현실적으로 실용화 할 수 있는 시스템 개발을 해내는 것이 무엇보다도 시급하고 중요한 문제이다. 앞으로 더욱 심도있게 연구하여 주요 하천에 대한 홍수예경보 시스템 구축이 절실히 요구된다.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.107-114
• /
• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.

• Journal of Astronomy and Space Sciences
• /
• v.24 no.4
• /
• pp.285-296
• /
• 2007

The Precipitable Water Vapor (PWV) from GPS with high resolution in terms of time and space might reduce the limitations of the numerical weather prediction (NWP) model for easily variable phenomena, such as precipitation and cloud. We have converted to PWV from Global Positioning System (GPS) data of Korea Astronomy and Space Science Institute (KASI) and Ministry of Maritime Affairs & Fisheries (MOMAF). First of all, we have selected the heavy rainfall case of having a predictability limitation in time and space due to small-scale motion. In order to evaluate the effect for GPS PWV, we have executed the sensitivity experiment with PWV from GPS data over Korean peninsula in the Weather Research & Forecasting 3-Dimensional Variational (WRF-3DVAR). We have also suggested the direction of further research for an improvement of the predictability of NWP model on the basis of this case.