• 한국관개배수논문집
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• 제7권2호
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• pp.66-74
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• 2000

The purpose of this study is to obtain land cover classification map by using remotely sensed data : Landsat TM and KOMPSAT-1 EOC, and to estimate SCS-CN direct runoff by using point rainfall(Thiessen network) and spatial rainfall(surface interpolation) f

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.1073-1080
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• 1996

The impact of the input and output probability distributions on the performance of neural networks to forecast two year peak stream flow (cubic meters per second) is examined for two major river basins of the US. The neural network input consisted of drainage area(square kilometers ) and elevation (meters). When data are normally distributed , the neural networks predict much better than when the data are non-normal and have larger tails in their distributions.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• 한국방재학회 논문집
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• 제8권5호
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• pp.129-135
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• 2008

본 연구에서 미국 환경청에서 개발한 SWMM5 모형에 배수갑문 모의기능을 추가하여 수정한 USWMM 모형을 개발했다. 개발된 USWMM의 적용성을 검증하기 위해 안성천 유역을 81개의 소유역으로 분할하고 실제 강우사상을 적용하여 배수위 해석을 수행하고 동연수위표와 평택수위표 및 배수갑문 지점의 관측수위를 대상으로 검 보증을 시도하였다. 그 결과 관측치와 매우 근사했으며, HEC-HMS와 HEC-RAS로 모의한 결과보다 우수한 것으로 나타났다. 그 이유는 USWMM은 HEC-RAS에서 누락된 불완전수중오리피스 흐름과 배수갑문의 관리수위를 설정할 수 있어 실제 배수갑문 운영 현장상황을 동일하게 모의할 수있기 때문으로 보인다. 따라서, 배수갑문과 연계된 하천 배수위 해석에서 USWMM을 범용적인 도구로 활용할 수 있을 것으로 기대된다.

• 대한원격탐사학회지
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• 제10권1호
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• pp.43-53
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• 1994

삼림을 베어서 태우고 난 직 후의 라오스 북부 화전지역의 MOS-1 위성영상을 처리하여 유역분지 단위로 화전지역의 면적을 산출하였으며, 지형도상에서 얻어낸 하계망과의 상관관계를 분석하기 위하여 PC Arc - Info의 GIS software를 이용하였다. 그 결과 화전의 분포비율이 높은 유역분지에서는 1차수하천의 분기율도 높게 나타남을 알수 있으며, 라오스에 있어서 화전이 지표 의 침식과 토지의 황폐화를 초래하여 여러가지 환경문제를 유발시키는 원인이 된다는 것이 규명 되었다.

• Current Optics and Photonics
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• 제7권5호
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• pp.511-517
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• 2023

Pipelines play an important role in urban water supply and drainage, oil and gas transmission, etc. This paper presents a technique for pattern recognition of fiber optic vibration signals collected by a distributed vibration sensing (DVS) system using a deep learning residual network (ResNet). The optical fiber is laid on the pipeline, and the signal is collected by the DVS system and converted into a 64 × 64 single-channel grayscale image. The grayscale image is input into the ResNet to extract features, and finally the K-nearest-neighbors (KNN) algorithm is used to achieve the classification and recognition of pipeline damage.

• 한국수자원학회논문집
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• 제32권5호
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• pp.565-577
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• 1999

프랙탈 기하학은 불규칙적이고 복잡한 자연 현상을 수학적으로 나타낼 수 있는 방법을 제시해 줄 수 있으며, 자기상사성을 가지고 있는 하천의 형상을 비롯한 하도망의 구성은 프랙탈 차원을 가지고 있는 프랙탈 현상이라 할 수 있다. GIUH란 유역의 수문학적 응답인 IUH에 하천의 지형학적인 특성을 적용한 강우-유출 모형으로, Horton의 차수비를 이용하여 지형학적인 특성을 반영할 수 있으며 하천 유역에서 프랙탈 차원은 길이비, 면적비, 분기비 등 Horton의 차수비를 이용하여 산정할수 있다. 프랙탈 GIUH 모형을 제시하였다. 프랙탈 GIUH 모형은 Rosso(1984)가 제시한 GIUH-Nash 모형의 형상계수와 규모계수 등의 매개변수 산정시 유역의 자기상사성을 대변할 수 있는 프랙탈 차원을 직접 적용하였으며, 하천의 길이비와 분기비 만의 함수로 나타내었다.

• Environmental Engineering Research
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• 제15권2호
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• pp.123-126
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• 2010

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ($1.5\;{\times}\;10^5$ and $1.62\;{\times}\;10^6\;m^2$) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

• 물과 미래
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• 제18권2호
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• pp.143-152
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• 1985

Horton의 지형법칙을 수문응답의 직접변수로 결합시켜 유출수문곡선을 재현하는 방법을 제시하였다 Itrube 등의 지형학적 순간단위도(Geomorphologic IUH) 이론과 Bras 등의 지형기후학적 순간단위도(Georphoclimatic IUH) 이론을 Sukekawa가 제시한 홍수유출모델에 적용시켜, 지형인자 및 기후특성이 고려된 수문응답함수를 재구성하였다. 분석결과 본 이론의 실제 유역에 대한 적용이 우수하였으며, 유역을 m(1∼4)개의 저수지로 모델화할 때, 유역면적의 대소에 관계없이 유역을 2개의 저수지로 가정한 m=2인 모델이 가장 적합한 것으로 나타났다. 또한 하천 차수법을 이용 유역을 모델화함으로서 본 이론의 확대해석이나 일반화가 이루어질 수 있을 것으로 판단되었다.

• Geomechanics and Engineering
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• 제22권2호
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• pp.133-142
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• 2020

The evolution of the mining-induced fracture network formed during longwall top coal caving (LTCC) has a great influence on the gas drainage, roof control, top coal recovery ratio and engineering safety of aquifers. To reveal the evolution of the mining-induced stress and fracture network formed during LTCC, the fracture network in front of the working face was observed by borehole video experiments. A discrete element model was established by the universal discrete element code (UDEC) to explore the local stress distribution. The regression relationship between the fractal dimension of the fracture network and mining stress was established. The results revealed the following: (1) The mining disturbance had the most severe impact on the borehole depth range between approximately 10 m and 25 m. (2) The distribution of fractures was related to the lithology and its integrity. The coal seam was mainly microfractures, which formed a complex fracture network. The hard rock stratum was mainly included longitudinal cracks and separated fissures. (3) Through a numerical simulation, the stress distribution in front of the mining face and the development of the fracturing of the overlying rock were obtained. There was a quadratic relationship between the fractal dimension of the fractures and the mining stress. The results obtained herein will provide a reference for engineering projects under similar geological conditions.