• KCID journal
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• v.7 no.2
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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

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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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.

• Proceedings of the KSRS Conference
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• 2004.10a
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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.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.129-135
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• 2008

USWMM was developed as a drainage analysis system for estuarine urban areas by adding sluice gates on existing EPA SWMM5 through this study. For the purpose of reviewing, Ansungchon river was modeled with USWMM and calibration and verification were attempted at three observation stations. In comparison, another approach using HEC-HMS and HEC-RAS was applied to the area under the same condition. It turned out that USWMM resulting values were closer to the observed values than those of the HEC-HMS and HEC-RAS approach. USWMM's flow simulation through sluices were more realistic to sluice operation fields by adding incomplete submerged orifice flow equation and maintenance water level. In sum, USWMM can be seen as a general purpose tool for estuarine urban drainage analysis system.

• Korean Journal of Remote Sensing
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• v.10 no.1
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• pp.43-53
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• 1994

By Using MOS-1 satellite image(taken on 24 April 1990, after slash and burn), Shifting cultivation areas were estimated for the sub-basin area. In tropical region to analyse the correlation between shifting cultivation rate and bifurcation rate network which was calculated from topographic map, PC Arc - Info and IDRISI GIS software were used. As the distribution rate of shifting cultivation increases, the bifurcation rate is high. From the correlation analysis between the shifting cultivation and drainage network, it was found that shifting cultivation leads to land degradation and head erosion at the stream valley. To prevent such problems, it is mecessary that shifting cultivation areas should be converted to permanent paddy fields.

• Current Optics and Photonics
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• v.7 no.5
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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.

• Journal of Korea Water Resources Association
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• v.32 no.5
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• pp.565-577
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• 1999

The geometric patterns of a stream network in a drainage basin can be viewed as a "fractal" with fractal dimensions. Fractals provide a mathematical framework for treatment of irregular, ostensively complex shapes that show similar patterns or geometric characteristics over a range of scale. GIUH (Geomorphological Instantaneous Unit Hydrograph) is based on the hydrologic response of surface runoff in a catchment basin. This model incorporates geomorphologic parameters of a basin using Horton's order ratios. For an ordered drainage system, the fractal dimensions can be derived from Horton's laws of stream numbers, stream lengths and stream areas. In this paper, a fractal approach, which is leading to representation of a 2-parameter Gamma distribution type GIUH, has been carried out to incorporate the self similarity of the channel networks based on the high correlations between the Horton's order ratios. The shape and scale parameter of the GIUH-Nash model of IUH in terms of Horton's order ratios of a catchment proposed by Rosso(l984J are simplified by applying the fractal dimension of main stream length and channel network of a river basin. basin.

• Environmental Engineering Research
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• v.15 no.2
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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.

• Water for future
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• v.18 no.2
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• pp.143-152
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• 1985

A method is suggested for the reappearance of a surface runoff hudorgraph of a river basin by linking the hydrologic response of a catchment represented by the instantaneous unit hydrograph(IUH) with the Horton's empirical gemorphologic laws. The geomorphologic theory of the IUH developed by G. Itrube et al. and the geomorphoclimatic theory of the IUH developed by Bras et al. are used to derive the new hydrologic response function in consideration of geomorphologic parameters and climatic characteristics by applying to Sukekawa's rainfall-runoff model. The derived response function was tested for on some observed hydrographs in a natural watershed and showed promising, and by considering a drainage basin as m(1∼4) identical linear reservoir in series, it was founded that the model(m=2) is most applicable to predict hydrologic response regardless of the size of basins. A modelization algorithm of a basin using Sthahler's ordering scheme of drainage network will give good result in analysis of the surface runoff huydrograph by the method of this study.

• Geomechanics and Engineering
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• v.22 no.2
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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.