• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.140-143
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• 2008

This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.1 no.1
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• pp.95-106
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• 1995

The small-waterplane-area-twin-hull(SWATH) ship has been recognized as a promising high performance ship because of her superior seakeeping characteristics and large deck area for various operations compared to the conventional monohull ship. significant advances in analytical technics for the prediction of the ship motions, wave loads and structural responses, structural fatigue and its prediction, and hull vibration for ship motions, wave loads and structural responses, structural fatigue and its prediction, and hull vibration for SWATH ship have been much developed during the last twenty years. Based on these developments in technology an integrated computational procedures for prediction wave loads and structural responses can be used to get a accurate results. But the major problem of SWATH ship's structural design is the accurate prediction of structural responses by the maximum critical loads likely to be experienced during the life of SWATH. To get a easier and safer computational procedures and the analytical approach for determining the accurate structural responses, a case study has been presented through the project experienced.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.92-94
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• 2001

This paper investigated the real-time generator swing prediction by some researchers. And the first swing stability assessment based on EAC(Equal-Area Criterion) by using phasor measurement unit is proposed. Also we proposed the multi-swing prediction techniques, which is to estimate system parameters by using least square method / extrapolation with phasor measurement units. And the multi-swing prediction is performed with the estimated parameters. Future works are necessary to verify the proposed approaches in this paper.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.601-604
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• 2006

This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.21-27
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• 2004

In an urban area telecommunication using wireless system, the accurate prediction and analysis of wave propagation characteristics are very important to determine the service area optimized selection of base station, and eel design, etc. In the stage of these analyses, we have to present the propagation prediction mood which is varied with the type of antenna, directional angle, and configuration of the ground in our urban area in addition we need to perform an analysis of the conventional mode which is similar to ours and dig out the parameters to evaluate the wave environment before the cell design for the selected area. In this paper, we propose a wave propagation prediction model concerning the topography and obstacles in our urban area. We extract the parameters and apply them to the proposed wave environment for the simulation analyzing the propagation characteristics. Throughout these analyzing procedure, we extracted the essential parameters such as the position of the base station, the height of topography, and adequate type and height of the antenna with our preferable cuteness.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2471-2478
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• 2020

Pumps are essential machinery in the various industries. With the development of high-speed and large-scale pumps, especially high energy density, high requirements have been imposed on the vibration and noise performance of pumps, and cavitation is an important source of vibration and noise excitation in pumps, so it is necessary to improve pumps cavitation performance. The modern pump optimization design method mainly adopts parameterization and artificial intelligence coupling optimization, which requires direct correlation between geometric parameters and pump performance. The existing cavitation performance calculation method is difficult to be integrated into multi-objective automatic coupling optimization. Therefore, a fast prediction method for pump cavitation performance is urgently needed. This paper proposes a novel cavitation prediction method based on impeller pressure isosurface at single-phase media. When the cavitation occurs, the area of pressure isosurface S_iso increases linearly with the NPSH_a decrease. This demonstrates that with the development of cavitation, the variation law of the head with the NPSH_a and the variation law of the head with the area of pressure isosurface are consistent. Therefore, the area of pressure isosurface S_iso can be used to predict cavitation performance. For a certain impeller blade, since the area ratio R_s is proportional to the area of pressure isosurface S_iso, the cavitation performance can be predicted by the R_s. In this paper, a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments, which will greatly accelerate the pump hydraulic optimization design.

• The KSFM Journal of Fluid Machinery
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• v.10 no.5
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• pp.41-45
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• 2007

The effects of channel area on the performance of regenerative type fuel pump were numerically studied by commercial CFD code (ANSYS CFX-10). To examine the effects of channel area, the shapes of the side channel and blade were simplified. The channel area affected the flow characteristics of the internal recirculation flow between the side channel and the blade groove and also made a difference in the overall performance. These loss mechanism with circulation flow were adopted as a loss coefficient in the performance prediction program. The loss coefficient was newly derived from the results of calculations with different channel area, and compared with the experimental results in the reference paper and used to modify the performance prediction program. The circulation flow characteristics with different channel area, which is related with loss mechanism, were also discussed with the results of 3-dimensional flow calculations.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.885-893
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• 2013

This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.