• Economic and Environmental Geology
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• v.40 no.1 s.182
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• pp.115-128
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• 2007

This study proposed a modified logistic regression model for a probabilistic prediction of debris flow on natural terrain at the granitic rock area. The modified model dose not contain any categorical factors that were used in the previous model and secured higher reliability of prediction than that of the previous one. The modified model is composed of lithology, two factors of geomorphology, and three factors of soil property. Verification result shows that the prediction reliability is more than 86%. Using the modified regression model, the landslide prediction maps were established. In case of Sacheon area, the prediction map showed that the landslide occurrence was not well corresponded with the model since, even though the forest-fred area was distributed on the center of the model, no factors were considered for the landslide predictions. On the other hand, the prediction model was well corresponded with landslide occurrence at Jumunjin-Yeongok area. The prediction model developed in this study has very high availability to employ in other granitic areas.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.73-82
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• 2011

Recent advancement in traffic surveillance systems has allowed the researchers to obtain more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading and following vehicles in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real-time based on the analysis of vehicular movements. The methodology presented in this study consists of three components. The first predicts vehicle position and speed every second using a Kalman filtering technique. The second estimates the probability for the vehicle's trajectory to belong to either 'changing lane' or 'going straight'. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component calculates crash probability by employing an exponential decay function that uses time-to-collision (TTC) between the subject vehicle and the front vehicle. The result of this study is expected to be adapted in developing traffic control and information systems, in particular, for crash prevention.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.1
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• pp.217-229
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• 1994

A probabilistic method was employed to compare the prediction accuracy of axial and volumetric strains of Lade's double surface model with that of single surface model. Several experiments were conducted to examine the variabilities of soil parameters for two models using Back-ma river sand. Mean values and standard deviations of soil parameters obtained from experimental data were used for the evaluation of the uncertainty of analyzed strains by the first order approximation. It is shown that the variabilities of parameters in the single surface model are more consistent than those of the double surface model. However, in the accuracy of axial strain by probabilistic analysis, double surface model is more stable than single surface model. It is also shown that two models are excellent in view of the accuracy of the volumetric strain. The method given in this paper may be effectively utilized to estimate the constitutive model because other results of the comparison of two models coincide with those of this paper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.21-27
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• 2011

When execute runoff forecasting, can not remove perfectly uncertainty of forecasting results. But, reduce uncertainty by various techniques analysis. This study applied various forecasting techniques for runoff prediction's accuracy elevation in Gongju basin. statics techniques is ESP, Period Average & Moving average, Exponential Smoothing, Winters, Auto regressive moving average process. Authoritativeness estimation with results of runoff forecasting by each techniques used MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RRMSE (Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC (Theil Inequality Coefficient). Result that use MAE, RMSE, RRMSE, MAPE, TIC and confirm improvement effect of runoff forecasting, ESP techniques than the others displayed the best result.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.85-89
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• 2005

도시발달과 인구증가로 인해 오늘날의 수자원 관리와 계획은 복잡하고 그 중요성은 더욱더 커지고 있으며, 인구와 재산의 집중현상으로 인하여 사소한 수문재해로 인해 막대한 인명과 재산피해를 초래될 수 있다. 이런 이유들로 인해 정확한 수문예측과 이를 통한 적절한 수자원 관리는 그 어느 때보다 중요한 인자로 인식되고 있다. 본 연구에서는 수문예측을 통한 소규모 댐으로의 정확한 월유입량 예측을 실시하여 실측유입량과 비교$\cdot$분석함으로서 수자원관리의 효율성을 향상시키고자 하였다. 수문예측을 위해서 확률론적 예측이 가능한 앙상블 예측기법(Ensemble Prediction Method)을 적용하였으며 과거 1968-1997년까지의 강우데이터와 수정 TANK모형을 이용하여 1998부터 2002년까지의 성주댐의 월유입량 앙상블을 생성하였다. 수문예측뿐만 아니라 유입량예측의 정확성을 향상시키기 위해 수정 TANK모형의 매개변수를 최적화기법 중의 하나인 유전자알고리즘을 이용하여 매개변수를 최적화하였으며 평창강유역과 보청천유역의 실측데이터를 이용하여 모형의 검증을 실시하였다. 또한 강우발생시 과소하게 유출량이 산정되는 것을 보완하기 위해 매개변수를 평수기와 홍수기의 구분하여 모형을 적용하였다. 본 연구에서 제시된 앙상블 예측기법과 최적화된 수정 TANK모형을 이용하여 댐의 수자원을 관리한다면 효율적인 관리가 이루어 질 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.45-59
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• 2003

The method in this study, which is more efficiency than the existing method, propose the optimal rehabilitation model based on the deterioration prediction of the laying pipe by using the deterioration survey method of the water distribution system. The deterioration prediction model divides the deterioration degree of each pipe into 5 degree by using the probabilistic neural network. Also, the optimal residual durability is estimated by the calculated deterioration degree in each pipe and pipe diameter. The optimal rehabilitation model by integer programming base on the shortest path can calculate a time and cost of maintenance, rehabilitation, and replacement. Also, the model is divided into budget constraint and no budget constraint. Consequently, the model proposed by the study can be utilized as the quantitative method for the management of the water distribution system.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.45-55
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• 2005

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.

• Korean Journal of Logic
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• v.12 no.1
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• pp.45-87
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• 2009

이 글에서 필자는 결정론적 인과를 토대로 속성 수준의 인과와 사건 수준의 인과의 연관성을 주장하는 하우스만(Hausman 1998)의 이론을 비판하고 두 수준의 인과의 관계를 바르게 이해하는 데 무엇이 필요한지를 제시한다. 하우스만은 결정론과 배경 조건의 다양성을 토대로 그리고 비결정적 상황에서는 확률에 대한 결정론적 인과를 토대로, 속성 수준의 인과는 사건 수준의 인과에서 도출된다는 의미에서 속성 수준의 인과는 사건 수준의 인과의 일반화라고 주장한다. 필자는 그 관계에 대한 문제를 제기하고 이 문제는 사건 수준의 인과에 본질적인 인과 연결을 주목하지 않은 채 변수들 간의 의존 관계만으로 두 수준의 인과의 관계를 단순히 해명하는 데에 있다고 지적한다. 필자는 두 수준의 인과의 관계는 단순히 한 가지 관점이나 방식으로 파악될 수 없고 해명, 설명, 예측 둥 다양한 관점에서 복합적으로 파악되어야 한다고 주장한다. 특히 사건 수준의 인과는 속성 수준의 인과에 개념적으로 의존하는 관계를 주목한다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.2
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• pp.62-72
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• 2019

Due to the difficulties in forecasting the intensity and the source location of tsunami the countermeasures prepared based on the deterministic approach fail to work properly. Thus, there is an increasing demand of the tsunami hazard analyses that consider the uncertainties of tsunami behavior in probabilistic approach. In this paper a fundamental study is conducted to perform the probabilistic tsunami hazard analysis (PTHA) for the tsunamis that caused the disaster to the east coast of Korea. A logic tree approach is employed to consider the uncertainties of the initial free surface displacement and the tsunami height distribution along the coast. The branches of the logic tree are constructed by reflecting characteristics of tsunamis that have attacked the east coast of Korea. The computational time is nonlinearly increasing if the number of branches increases in the process of extracting the fractile curves. Thus, an improved method valid even for the case of a huge number of branches is proposed to save the computational time. The performance of the discrete weight distribution method proposed first in this study is compared with those of the conventional sorting method and the Monte Carlo method. The present method is comparable to the conventional methods in its accuracy, and is efficient in the sense of computational time when compared with the conventional sorting method. The Monte Carlo method, however, is more efficient than the other two methods if the number of branches and the number of fault segments increase significantly.