• Journal of the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.458-471
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• 2013

This paper addresses the limitation of a map comparison metric entitled Figure of Merit through employing a simple land change model. The metric was originally designed to overcome limitations of other existing statistics, such as Kappa, when assessing predictive accuracy of land change models. A series of comparisons between null and predicted outcomes at multiple resolutions as well as a multi-resolution Figure of Merit analysis techniques of validation are compared for spatially segregated calibration and validation datasets. The Figure of Merit at the null resolution in this paper was 57%, although future research must be done to determine if this was simply a coincidence. A Figure of Merit greater than 50% would seem to represent a "Resolution of Merit" in that the Figure of Merit at that resolution becomes greater than the error. Thus, these two metrics should be used in tandem to assess predictive accuracy of a land change model.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5225-5231
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• 2015

Background: One of the most prevalent cancers in whole world is skin cancer and its prevalence is growing. The present research sought to estimate relative risk of morbidity and mortality due to skin cancer. Materials and Methods: In this cross-sectional study. The required data were gathered from the registered cancer reports of Cancer Control Office in the Center for Non Communicable Disease of the Iranian Ministry of Health (MOH). The data were extracted at province level in the time span of 2008-10. WINBUGS software was used to analyze the data and to identify high risk regions. ArcGIS10 was utilized to map the distribution of skin cancer and to demonstrate high risk provinces by using classic and fully Bayesian models taking into account spatial correlations of adjacent regions separately for men and women. Results: Relative risk of morbidity for women in Yazd and for men in Kurdistan and relative risk of mortality for women in Bushehr and for men in Kohgiluyeh were found to be the highest. Bayesian model due to regarding adjacent regions correlation, have precise estimation in comparing to classical model. More frequent epidemiological studies to enact skin cancer prevention programs. Conclusions: High risk regions in Iran include central and highland regions. Therefore it is suggested that health decision makers enact public education, using anti UV creams and sunglasses for those parts as a short preventing program.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.959-969
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• 2018

The aging water pipes induce various problems, such as water supply suspension due to breakage, insufficient water pressure, deterioration of water quality, damage by sink holes, and economic losses due to water leaks. However, it is impractical and almost impossible to repair and/or replace all deteriorated water pipes simultaneously. Hence, it is required to quantitatively evaluate the deterioration rate of individual pipes indirect way to determine the rehabilitation order of priority. In this study, ANN(Artificial Neural Network)-Clustering method is suggested as a new approach to assess and assort the water pipes. The proposed method has been applied to a water supply network of YG-county in Jeollanam-do. To assess the applicability of the model, the evaluation results were compared with the results of the Numerical Weighting Method (NWM), which is being currently utilized in practice. The assessment results are depicted in a water pipe map to intuitively grasp the degree of deterioration of the entire pipelines. The application results revealed that the proposed ANN-Clustering models can successfully assess the water pipe deterioration along with the conventional approach of NWM.

• Water for future
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• v.28 no.4
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• pp.185-194
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• 1995

The objective of this study is to explore scale problem and to analyze the relations between scale and geomorphologic parameters of the rainfall-runoff model. Generally, measurement and calculation of geomorphologic parameters rely on and are sensitive to the resolution of source information available. Therefore, rainfall-runoff models using geomorphologic parameters should take account of the effects of the map scale used in their development. The derived rainfall-runoff model considering scale problem in this research is the GIUH type model, that is a basin IUH consisting of the channel network response and hillslope response. The cannel network response is computed by means of the diffusion analogy transformed from linearized St. Venant equation and hillslope response is calculated by 2-parameter gamma distribution function. Representing geomorphologic structure of the channel network and initial distribution of its response is width function. This width function is derived by fractal theory and Melton's law to consider scale problems and is weighted by the source location function (SLF) proposed in this research to increase the applicability.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2262-2270
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• 1999

In this paper, we describe gesture recognition algorithm using computer vision sensor and HMM. The automatic hand region extraction has been proposed for initializing the tracking of the tele-operation gestures. For this, distance informations(disparity map) as results of stereo matching of initial left and right images are employed to isolate the hand region from a scene. PDOE(positive difference of edges) feature images adapted here have been found to be robust against noise and background brightness. The KNU/KAERI(K/K) gesture instruction set is defined for tele-operation in atomic electric power stations. The composite recognition model constructed by concatenating three gesture instruction models including pre-orders, basic orders, and post-orders has been proposed and identified by discrete HMM. Our experimental results showed that consecutive orders composed of more than two ones are correctly recognized at the rate of above 97%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2005.11a
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• pp.81-104
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• 2005

Accurate 3D models in urban areas are essential for a variety of applications, such as virtual visualization, CIS, and mobile communications. LiDAR(Light Detection and Ranging) is a relatively new technology for directly obtaining 3D points. Because Manual 3D data reconstruction from LiDAR data is very costly and time consuming, many researchs is focused on the automatic extraction of the useful data. In this paper, we classified ground and non-ground points data from LiDAR data by using filtering, and we reconstructed the DTM(Digital Terrain Model) using ground points data, buildings using nonground points data. After the reconstruction, we assessed the accuracy of the DTM and buildings. As a result of, DTM from LiDAR data were 0.16m and 0.59m in high raised apartments areas and low house areas respectively, and buildings were matched with the accuracy of a l/5,000 digital map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.153-158
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• 2006

A new High Resolution Geometry or HRG imaging instrument is developed by CNES to be carried on-board SPOT 5. The HRG instrument offers a higher ground resolution than that of the HRV/HRVIR on SPOT 1 - 4 satellites. The field width of HRG is 60 km, same as SPOT constellation. With two HRG instruments, a maximum swath of 120 km at 5 m resolution can be achieved. The generation of Digital Elevation Models (DEMs) from satellite stereo images scores over conventional methods of DEM generation using topographic maps and aerial photographs. This global availability of satellite images allows for quicker data processing for an equivalent area. In this study, a HRG stereo images of SPOT 5 over JECHEON has been used with Leica Photogrammetry Suite OrthoBASE Pro tool for the creation of a digital elevation model (DEM). The extracted DEM was compared to the reference DEM obtained from the contours of digital topographic map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.6
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• pp.687-695
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• 2011

Many internet portals provide maps with spatial information services. Recently, various images including aerial, satellite, street view, and photo-realistic 3D city models are provided as well as maps. This study suggested a method for geometric correction of the panoramic aerial images in the internet portal and 3D building modeling using information which is available in the internet. The key of this study is to obtain all necessary data easily from internet without restrictions. Practically, the ground control coordinates could be available from geo-referenced internet maps, and stereo pairs of the aerial images and close-range photographs for photo-realistic object modeling are provided by the internet service. However, the ground control points are not suitable for accurate mapping. RMSE of the plotting was about 9 meters and reduced upto 4 meters after coordinate transformation. The proposed methods would be applicable to various applications of photo-realistic object modeling which do not require high accuracy.