• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.155-165
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• 1998

음성자동인식을 위한 통계적 방법으로 은닉마코프모형이 널리 사용되고 있다. 이산형 은닉마코프모형보다 인식률이 우수한 연속형 은닉마코프모형을 고려하였으며, 인식을 위한 비터비(Viterbi) 알고리즘을 병렬화시켜 인식속도를 빠르게 하는 인식 알고리즘을 제안하였다. 제안된 방법으로 실험을 통하여 인식률과 인식속도 개선률(speed-up)을 살펴보았다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.477-479
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• 2011

본 논문에서는 1차원 은닉 마코프 모델을 2차원으로 확장하기 위하여 노드들의 마코프 특성이 인과적인 관계를 갖는 마코프 메쉬 모델을 이용하여 완전한 2차원 HMM의 구조를 갖는 모델을 제안한다. 마코프메쉬 모델은 이웃시스템을 통하여 이전의 시점을 정의하고, 인과적인 관계를 통하여 전이확률의 계산을 가능하게 한다. 또한 영상의 최적의 분할을 위하여 계층적 디리슐레 과정을 사전분포로 두어 고정된 상태의 수가 아닌 무한의 상태 수를 갖는 2차원 HMM을 제안한다. HDP로 정의된 사전분포와 관측된 표본 자료의 정보를 갖는 우도함수를 결합한 사후분포의 베이스 추정은 깁스샘플링 알고리즘을 이용하여 계산된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.163-163
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• 2018

미래 수문 분석을 위한 기후변화 연구는 전 세계적으로 많이 수행되어 왔다. 하지만 불확실성 요소로 인해 연구 결과를 활용하는데 있어 여전히 한계가 있다. 따라서 장기적 측면의 기후변화에 대한 연구와 함께 단기간의 엘리뇨, 라니냐와 같은 자연적 기후시스템의 변동에 대한 연구도 현재 진행되고 있다. 본 연구에서는 IRI 연구소에서 매월 전지구 관측자료로 4-7개월 예측을 수행한 GCM 모형 자료를 활용하여 강우 발생을 예측하였다. 한국의 금강유역을 대상유역으로 하였으며, 계절에 따른 강우 변동성을 고려하기 위해 비동질성 은닉 마코프 모형(Nonhomogeneous Hidden Markov Model, NHMM)을 이용하여 일 강우를 모의하였다. 본 연구 결과는 강우 모의를 통한 자연 재난에 대한 예측의 정확도를 향상시키는 새로운 방법론을 제시하는데 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.111-111
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• 2020

하천환경과 기후의 변화로 인해 수질오염 과정의 메커니즘이 더욱 복잡해짐에 따라 다양한 요인을 고려한 불확실성 평가 연구가 요구되고 있다. 하천 수질 중에서도 부영양화 문제는 특히 개발로 인한 하천환경 변화 이후 사회 정치적 논점이 되어왔다. 본 연구에서는 지난 7년 동안의 수질 변화의 전반적인 양상을 조사하였으며, 클로로필-a(Chl-a, chlorophyll-a) 농도의 시공간적 의존성의 효과적으로 고려하기 위해 기계학습 기반 분류(classification) 접근법인 다변량 은닉 마코프 모형(MHMM, multivariate hidden Markov model)을 사용하였다. 월 단위 수질 및 수문 자료를 사용하여 Chl-a의 변동성을 군집화하여 수질 상태의 익월 천이확률을 효과적으로 추정하였다. Chl-a와 수질 및 수문기상 조건의 관계를 평가하였으며, 결과적으로 수질 상태의 시공간적 전이가 정확하게 식별되었고 이의 잠재적 원인에 대하여 논의하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.145-153
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• 2014

Several studies regarding drought indices and criteria have been widely studied in the literature. If one defines the onset, severity, and end of droughts, in general, a certain threshold needs to be set to assess the drought events. However, the uncertainty associated with the threshold is a critical problem in drought analysis. To take full advantage of the inherent features in the rainfall series, a Hidden Markov Model (HMM) based probabilistic drought analysis was proposed rather than using the existing threshold based analysis. As a result, the proposed HMM based probabilistic drought analysis scheme shows better performance in terms of defining drought state and understanding underlying characteristics of the drought. In addition, the HMM based approach is capable of quantifying the uncertainties associated with the classifying meteorological drought condition in a systematic way.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.435-446
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• 2014

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966~2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.969-979
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• 2015

This study applied a probabilistic-based hidden Markov model (HMM) to better characterize drought patterns. In addition, a copula-based bivariate drought frequency analysis was employed to further investigate return periods of the current drought condition in year 2015. The obtained results revealed that western Kangwon area was generally more vulnerable to drought risk than eastern Kangwon area using the 40-year data. Imjin-river watershed including Cheorwon area was the most vulnerable area in terms of severe drought events. Four stations in Han-river watershed showed a joint return period exceeding 1,000 years associated with the drought duration and severity in 2014-2015. Especially, current drought status in Northern Han-river and Imjin-river watershed is most severe drought exceeding 100-year return period.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.545-554
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• 2019

The most drought assessments are based on a drought index, which depends on univariate variables such as precipitation and soil moisture. However, there is a limitation in representing the drought conditions with single variables due to their complexity. It has been acknowledged that a multivariate drought index can more effectively describe the complex drought state. In this context, this study propose a Copula-based drought index that can jointly consider precipitation and soil moisture. Unlike precipitation data, long-term soil moisture data is not readily available so that this study utilized a Gaussian Mixture Non-Homogeneous Hidden Markov chain Model (GM-NHMM) model to simulate the soil moisture using the observed precipitation and temperature ranging from 1973 to 2014. The GM-NHMM model showed a better performance in terms of reproducing key statistics of soil moisture, compared to a multiple regression model. Finally, a bivariate frequency analysis was performed for the drought duration and severity, and it was confirmed that the recent droughts over Jeollabuk-do in 2015 have a 20-year return period.