• Geomechanics and Engineering
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• 제8권5호
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• pp.631-647
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• 2015

Engineers may encounter unpredictable cavities, sinkholes and karst conduits while tunneling in karst area, and water inrush disaster frequently occurs and endanger the construction safety, resulting in huge casualties and economic loss. Therefore, an optimal classification method based on grey system theory (GST) is established and applied to accurately predict the occurrence probability of water inrush. Considering the weights of evaluation indices, an improved formula is applied to calculate the grey relational grade. Two evaluation indices systems are proposed for risk assessment of water inrush in design stage and construction stage, respectively, and the evaluation indices are quantitatively graded according to four risk grades. To verify the accuracy and feasibility of optimal classification method, comparisons of the evaluation results derived from the aforementioned method and attribute synthetic evaluation system are made. Furthermore, evaluation of engineering practice is carried through with the Xiakou Tunnel as a case study, and the evaluation result is generally in good agreement with the field-observed result. This risk assessment methodology provides a powerful tool with which engineers can systematically evaluate the risk of water inrush in karst tunnels.

• 한국관개배수논문집
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• 제17권2호
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• Water Engineering Research
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• 제3권4호
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• pp.259-267
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• 2002

South Africa has developed a policy and law that calls and provides for the equitable and sustainable use of water resources. Sustainable resource use is dependent on effective resource protection. Rivers are the most important freshwater resources in the country, and there is a focus on developing and applying methods to quantify what rivers need in terms of flow and water quality. These quantified and descriptive objectives are then related to specified levels of ecological health in a classification system. This paper provides an overview of an integrated and systematic methodology, where, fer each river, and each river reach, the natural condition and the present ecological condition are described, and a level/class of ecosystem health is selected. The class will define long term management goals. This procedure requires each ecosystem component to be quantified, starting with the abiotic template. A modified flow regime is modelled for each ecosystem health class, and the resultant fluvial geomorphology and hydraulic habitats are described. Then the water chemistry is described, and the water quality changes that are likely to occur as a consequence of altered flows are predicted. Finally, the responses to the stress imposed on the biota (fish, invertebrates and vegetation) by modified flow and water quality are predicted. All of the predicted responses are translated into descriptive and/or quantitative management objectives. The paper concludes with the recognition of active method development, and the enormous challenge of applying the methods, implementing the law, and achieving river protection and sustainable resource-use.

• 상하수도학회지
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• 제26권5호
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• pp.637-647
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• 2012

Recent series of flooding events in urban area has brought a growing concern on storage facilities as a major stormwater management method. The Korean Ministry of Environment has announced diverse plans to tackle the problem, including plans for multi-purpose storages which deal both the stormwater and wastewater. Even though storages can be categorized for different perspectives, classification of possible storages in urban area has not been throughly studied so far. This study investigated diverse references of urban storages and suggested systematic classifications on structural, functional and some other basis. Structural classification mainly concerns structural shape of facilities and includes (1)Cisterns & Rain barrels, (2)Forebays, (3)Dry basins, (4)Wet basins and (5)Constructed wetland. Those functions can be (1)flood prevention (2)water quality control and (3)reuse of stored water. Other criteria that categorize storages depend on (1)height, (2)location, (3)configuration, (4)depth, (5)site of the installation and (6)shape.

• 에너지공학
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• 제23권4호
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• pp.263-271
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• 2014

터빈 사이클의 성능 상태량을 결정하기 위한 보정 열 성능 분석은 발전소의 향상된 경제성 운전을 위해 요구된다. 본 연구에서는 유용하고 정확한 성능 분석을 위해서 산업 표준인 ASME PTC를 기분으로 하여 성능 데이터를 사용하여 주급수 유량의 영역별 판정 알고리듬을 개발하고 각 영역별 추정 알고리즘을 개발하였다. 추정 알고리즘은 측정 상태량의 상관관계를 기반으로 형상 분류를 제시하고, 이를 기반으로 서포트 벡터 머신 모델링을 이용하여 추정 모델을 구성하였으며, 서포트 벡터 머신 모델링의 우수성을 검증하기 위하여 신경 회로망 모델, 커널 회귀 모델과 비교하였다. 주급수 유량의 형상 분류 및 추정 모델은 터빈 사이클에서 정확한 보정 열 성능 분석을 제공함으로써 향상된 성능 분석에 기여할 것이다.

• 대한원격탐사학회지
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• 제35권4호
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• pp.561-571
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• 2019

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.1138-1140
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• 2005

• 물과 미래
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• 제30권1호
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• pp.21-26
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• 1997

• 한국콘텐츠학회논문지
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• 제9권8호
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• pp.1-8
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• 2009

IT 융합 기술의 발전에 따라 정부의 4대강 복원을 위한 마스터플랜이 구축되면서, 환경 친화적인 수질 오염 관리의 중요성이 부각되고 있다. 본 논문에서는 친환경 저수조의 수질 향상과 온라인 관리를 하기 위해서 저수조 자동 분류를 이용한 효과적인 수질 오염 관리를 제안하였다. 제안된 방법에서는 수질오염 평가의 7가지 요소들을 정의하였고 센서를 이용하여 수소이온농도(pH), 화학적 산소요구량(COD),부유 물질량(SS), 용존 산소량(DO), 대장균군수(MPN), 총인 (T-P), 총질소(T-N)에 따른 적합한 수질 오염 관리를 하였다. 저수조의 7가지의 수질 오염 요소간의 측정치를 평가하고 [1,9] 사이에 분포하도록 정규화하였다. 저수조 자동 분류를 이용한 수질 오염 관리 시스템의 성능 평가를 하기 위해 F-측정식을 이용하여 유용성을 검증하였다. 평가 결과, 기존 시스템에 대한 만족도의 차이가 통계적으로 의미가 있음을 증명하였다.

• 대한원격탐사학회지
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• 제23권3호
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• pp.181-188
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• 2007

The objective of this research was to investigate the optimal land cover classification algorithm for the monitoring of North Korea with MODIS multi-temporal data based on monthly phenological characteristics. Three frequently used land cover classification algorithms, ISODATA1), SMA2), and SOM3) were employed for this study; the land cover categories were forest, grass, agricultural, wetland, barren, built-up, and water body. The outcomes of the study can be summarized as follows. First, the overall classification accuracy of ISODATA, SMA, and SOM was 69.03%, 64.28%, and 73.57%, respectively. Second, ISODATA and SMA resulted in a higher classification accuracy of forest and agricultural categories, but SOM performed better for the built-up area, bare soil, grassland, and water. A possible explanation for this difference would be related to the difference of sensitivity against the vegetation activity. This would be related to the capability of SOM to express all of their values without any loss of data by maintaining the topology between pixels of primitive data after classification, while ISODATA and SMA retain limited amount of data after normalization process. Third, we can conclude that SOM is the best algorithm for monitoring the land cover change of North Korea.