• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.6 no.2
• /
• pp.23-28
• /
• 2010

Fluent ver.6.3 is used as CFD(Computational Fluid Dynamics) simulator to predict the performance of snow-melting system by geothermal pipes energy. As the results of this simulation, it is clearly shown that $50^{\circ}C$ of working fluid in to geothermal evaluated as more effect comparing to $45^{\circ}C$ of working fluid. The Surface temperature is come to $5^{\circ}C$ at 1m/s speed and $50^{\circ}C$ temperature of the working fluid.

• Journal of Korea Water Resources Association
• /
• v.57 no.1
• /
• pp.35-44
• /
• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.14 no.3
• /
• pp.188-202
• /
• 2011

Due to emissions of greenhouse gases caused by increased use of fossil fuels, the climate change has been detected and this phenomenon would affect even larger changes in temperature and precipitation of South Korea. Especially, the increase of temperature by climate change can affect the amount and pattern of snowfall. Accordingly, we tried to predict future snowfall and the snowfall pattern changes by using the downscaled GCM (general circulation model) scenarios. Causes of snow varies greatly, but the information provided by GCM are maximum / minimum temperature, rainfall, solar radiation. In this study, the possibility of snow was focused on correlation between minimum temperatures and future precipitation. First, we collected the newest fresh snow depth offered by KMA (Korea meteorological administration), then we estimate the temperature of snow falling conditions. These estimated temperature conditions were distributed spatially and regionally by IDW (Inverse Distance Weight) interpolation. Finally, the distributed temperature conditions (or boundaries) were applied to GCM, and the future snowfall was predicted. The results showed a wide range of variation for each scenario. Our models predict that snowfall will decrease in the study region. This may be caused by global warming. Temperature rise caused by global warming highlights the effectiveness of these mechanisms that concerned with the temporal and spatial changes in snow, and would affect the spring water resources.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.427-427
• /
• 2023

일반적으로 수자원가용량이라 하면 지표·지하·토양 등에 있는 모든 수자원의 양이라 할 수 있다. 정확한 수자원가용량의 평가를 위해서는 강수, 기온 등의 기상 예측의 정확도 확보가 우선되어야 하며, 지표 하에 보이지 않는 수자원의 양을 정확히 평가할 수 있어야 한다. 한국건설기술연구원은 2012년부터 세계기상기구(WMO, World Meteorological Organization)에서 수자원평가 부문 리더 역할을 수행하면서 회원국들에게 수자원가용량평가를 위한 '동적수자원평가시스템'의 개발을 제안하여 추진하였다. 그 결과, 동적수자원평가시스템(Dynamic Water resources Assessment Tool, DWAT)이 2017년 12월에 개발되었고, 2019년 5월에는 WMO 웹사이트 (https://public.wmo.int/en/water/dynamic-water-resources-assessment-tool)를 통해 193개 회원국에 보급되기 시작하였다. DWAT은 전 세계가 무료로 이용할 수 있는 수자원평가 도구로, 지하수, 용수이용 뿐만 아니라 지표수를 고려한 수자원계획 및 관리를 위해 중⋅소규모 하천 유역에 적용될 수 있다. 특히, 논 지역의 유출특성을 모의할 수 있는 모듈을 탑재하였으며, 고위도 및 고산지대의 수문학적 특성을 반영할 수 있는 융설 모듈이 포함되었고, 매개변수 최적화 기능도 포함되었다. WMO는 수자원분야 주요사업 중 하나인 "전지구 수문현황 및 전망 시스템(HYDROSOS, global HYDROlogical Status and Outlook System)" 사업을 추진하고 있다. 본 사업은 전지구 기상예보를 활용하여 주요 지점의 자연 유출량에 대한 현황과 예보를 수행하는 것을 목표로 한다. 2019년 6월 제18차 WMO 총회에서는 수자원분야 주요 사업인 HYDROSOS의 시범사업을 DWAT이 지원하는 것으로 의결되었다. 따라서 이러한 DWAT의 활용을 통해 대한민국의 수자원 평가 실무와 관련된 기술이 WMO 회원국에 지속적으로 보급될 것으로 판단된다.

• Korean Journal of Soil Science and Fertilizer
• /
• v.32 no.1
• /
• pp.31-38
• /
• 1999

Rainfall factor. R, and soil factor, K were estimated to use the Revised Universal Soil Loss Equation (RUSLE) to predict the amount of soil erosion from a land on slope in Kangwon-do, Korea. The average of R factor was 405 with a range from 251 to 601. The R factor differed among regions. The R factor at Taegwalryung, in the highland region, was 409 and those at Inje and Hongchon, in the mid mountainous regions, ranged from 310 to 493. The R factors at Wonju and Chuncheon, in the plain regions, ranged from 505 to 601. The R factors at Sokcho, Kangnung and Samchok, in the east coastal region, which ranged from 251 to 368, were lowee than those in the western part of the Taebaeg Mountains. The R factor during the winter including the effect of winter freezing and thawing was 12 to 30% of the annual average value in the east coastal and highland regions, while that in the western part of Taebaeg Mountains was lower than 7%. The average of K factor in the surface soil was 0.21 with a range from 0.06 to 0.42. The K factors of Odae and Weoljeong serieses were the lowest, while that of Imog was the highest. The average of K factor in the subsoil was 0.28 with a range from 0.07 to 0.45. The K factor of the subsoil was 1.3 times higher than that of top soil. The average of K factor in he soil including the effect of the gravel covering and percolation was 0.18 with a range from 0.03 to 0.33. In contrast. the K factor excluding the effect of the gravel covering was lower than this. The average of K factor in the frozen subsoil was 0.33, which was 1.6 times higher than that of the non frozen subsoil.