• Geomechanics and Engineering
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• 제20권2호
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 한국정보통신학회논문지
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• 제19권12호
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• pp.2779-2784
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• 2015

정확한 교통정보의 예측은 출발지로부터 목적지까지의 최적경로를 제공할 수 있으며, 이로 인해 시간과 비용의 절감 효과를 얻을 수 있다. 본 논문에서는 다양한 교통정보 예측 방법 중 확률 모델을 기반으로 교통정보를 예측하는 베이지안 네트워크 방법을 이용한다. 기존 연구에서는 베이지안 네트워크 예측 방법이 모든 시간대에서의 데이터를 학습에 사용하는 것과는 달리, 본 논문에서는 예측하고자 하는 시간대와 동일한 요일과 시간에 해당하는 데이터만을 선별적으로 학습에 사용한다. 서로 다른 두 가지 학습방법에 따른 예측 결과의 정확도는 일반적으로 많이 사용되는 MAPE(Mean Absolute Percentage Error)로 검증하였으며, 서울 시내 14개의 링크 구간에 대해 실험을 진행하였다. 실험결과는 본 논문에서 제안한 방법이 모든 시간대의 데이터를 학습에 사용한 방법에 비해 MAPE의 관점에서 더 높은 정확도를 가진 교통 예측 값을 계산할 수 있음을 보여준다.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.817-827
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• 2000

In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

• 대한안전경영과학회지
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• 제13권1호
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• pp.91-98
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• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.

• 한국지형학회지
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• 제24권2호
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• pp.91-101
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• 2017

Quantitative forecasting methods based on spatial data and geographic information system have been used in predicting the landslide location. This study compared the simulated results of logistic, Bayesian, and maximum entropy models to understand the uncertainties of each model and identify the main factors that influence landslide. The study area is Boeun gun where 388 landslides occurred in the year of 1998. The verification results showed that the AUC of the three models was 0.84. However, the landslide susceptibility distribution of Maxent model was different from those of the other two models. With the same landslide occurrence data, the result of high susceptible area in Maxent model is smaller than Logistic or Bayesian. Maxent model, however, proved to be more efficient in predicting landslide than the other two models. In Maxent's simulations, the responsible factors for landslide susceptibility are timber age class, land cover, timber diameter, crown closure, and soil drainage. The results suggest that it is necessary to consider the possibility of overestimation when using Logistic or Bayesian model, and forest management around the study area can be an effective way to minimize landslide possibility.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.303-303
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• 2018

안정적인 수자원 운용을 위해서는 정확한 유량예측 기술이 필요하다. 본 연구에서는 유량예측 정확도의 개선을 위해 베이지안 추론(Bayesian inference) 기법과 앙상블 유량 예측(Ensemble Streamflow Prediction, ESP) 기법의 결합을 통한 새로운 유량예측 기법(Bayesian ESP)을 제안하였다. ESP를 통한 유량 예보 앙상블은 베이지안 추론의 사전정보로 활용되며, 관측 유량과 ESP 전망 결과의 선형관계를 통해 우도함수가 추정된다. 우도함수는 관측 유량이 존재하는 과거 기간에 대한 ESP를 수행한 후 예보 시점의 관측 유량(concurrent observed flow)과 선행 관측 유량(lagged observed flow)과의 다중선형회귀 모형을 통해 추정된다. 사전정보와 우도함수는 정규분포로 가정되며, 따라서 최종 유량예측인 사후정보 역시 정규분포함수로 산정되게 된다. Bayesian ESP은 ESP에서 발생하는 강우-유출모형 오차의 개선을 통해 수문예측의 정확도를 개선하게 되며 정규분포함수로 최종 결과가 산정되므로 확률예보 형태의 수문 전망도 가능하다. 본 기법을 전국 35개 댐 유역에 시범적용을 한 결과, 모든 유역에서 기존 ESP 기법 대비 수문예측 정확도의 개선을 가져왔으며, 우도함수 추정에 있어 선행 유량의 포함 여부가 수문 예측 정확도의 추가적인 개선을 가져왔다. 본 기법은 주간 예보부터 계절 예보까지 탄력적으로 구축이 가능하며 적용 결과 리드 타임이 길어질수록 예측 능력이 감소되었지만 전체 구간에 있어서 Bayesian ESP 기법이 가장 우수한 예측 정확도를 보여주었다.

• 한국구조물진단유지관리공학회 논문집
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• 제8권4호
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• pp.185-193
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• 2004

현재의 교량의 유지관리 시에 적절한 보수 시기나 최적화된 유지관리 정책을 결정하기 위하여 교량의 성능저하를 정확히 예측하는 것은 가장 중요한 일이다 이률 위해 제안된 방법은 정량적 평가, 마르코프체인, 베이시안 추정법 등으로 구성되었다. 제안된 방법에 따라 국내의 콘크리트 슬래브 교량을 예로서 예측을 하여는데, 기존의 전문가 의견조사 빛 외관조사에 의한 예측보다 좀 더 합리적인 결과를 보여주었다.

• 제어로봇시스템학회논문지
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• 제15권9호
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.