• 한국농공학회논문집
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• 제62권4호
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• pp.33-43
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• 2020

In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundation accurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of Korea Meteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it is necessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is to establish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts was collected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfall was performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculated by extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundation probability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage from inundation.

• 한국농공학회논문집
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• 제62권1호
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• pp.17-27
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• 2020

The objective of this study was to assess the flood probability based on temporal distribution of forecasted-rainfall in Cheongmicheon watershed. In this study, 6-hr rainfalls were disaggregated into hourly rainfall using the Multiplicative Random Cascade (MRC) model, which is a stochastic rainfall time disaggregation model and it was repeated 100 times to make 100 rainfalls for each storm event. The watershed runoff was estimated using the Clark unit hydrograph method with disaggregated rainfall and watershed characteristics. Using the peak discharges of the simulated hydrographs, the probability distribution was determined and parameters were estimated. Using the parameters, the probability density function is shown and the flood probability is calculated by comparing with the design flood of Cheongmicheon watershed. The flood probability results differed for various values of rainfall and rainfall duration. In addition, the flood probability calculated in this study was compared with the actual flood damage in Cheongmicheon watershed (R² = 0.7). Further, this study results could be used for flood forecasting.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• 한국농공학회논문집
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• 제65권1호
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• pp.41-49
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• 2023

To utilize the hydraulic and hydrological models when simulating floods in agricultural watersheds, it is necessary to consider agricultural reservoirs, farmland, and farmland drainage system, which are characteristics of agricultural watersheds. However, most of them are developed individually by different researchers, also, each model has a different simulation scope, so it is hard to use them integrally. As a result, there is a need to link each hydraulic and hydrological model. Therefore, this study established an integrated flood simulation system for the comprehensive flood simulation of agricultural reservoir watersheds. The system can be applied easily to various watersheds because historical weather data and the SSP (Shared Socio-economic Pathways) climate change scenario database of ninety weather stations were built-in. Individual hydraulic and hydrological models were coded and coupled through Python. The system consists of multiplicative random cascade model, Clark unit hydrograph model, frequency analysis model, HEC-5 (Hydrologic Engineering Center-5), HEC-RAS (Hydrologic Engineering Center-River Analysis System), and farmland drainage simulation model. In the case of external models with limitations in conceptualization, such as HEC-5 and HEC-RAS, the python interpreter approaches the operating system and gives commands to run the models. All models except two are built based on the logical concept.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.187-187
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• 2022

우리나라의 농촌 유역은 크게 1) 상류에 위치한 농업용 저수지, 2) 저수지 방류부, 3) 저수지 하류하천, 4) 하류 농업 지대로 구성된다. 이들 모두 유역의 홍수·침수와 연관되어 있으나 각각의 설계 빈도가 서로 달라 일시에 수용 가능한 수자원의 양이 상이하다. 예컨대 극한 강우가 발생한 경우 PMP를 고려하여 설계된 저수지에서는 유입 홍수량이 통제될 수 있으나 50-200년 빈도로 설계된 하류하천에서는 측면 유입량 때문에 홍수가 발생할 수 있다. 따라서 유역의 홍수 확률을 산출할 때에는 유역 구성지역별 홍수 확률을 산정한 후 종합적으로 고려할 필요가 있다. 특히 농촌유역의 경우 하류하천 및 농경지의 설계 빈도 기준이 도시에 비해 낮아 유역 구성요소 간 처리 가능한 수자원 양의 차이가 크다. 따라서 본 연구에서는 농촌 유역을 대상으로 연구를 진행하였다. 한편, 최근 기후변화로 인해 극한 강우 사상의 빈도가 잦아짐에 따라 유역 내 홍수의 발생이 증가하고 있다. 따라서 기후변화에 따른 미래 농촌 유역의 홍수 발생 여부 파악이 필수적이다. 이에 본 연구에서는 CMIP 6 (Coupled Model Intercomparison Project Phase 6)의 GCM (General Circulation Model) 기상산출물을 농촌 유역에 적용함으로써 미래 농촌 유역의 홍수 발생 여부를 확인하고자 하였다. 또한, CMIP 6의 GCM 산출 기상자료의 시간 단위는 24시간 혹은 3시간으로 시간적 해상도가 낮으므로 유역 홍수 모의를 위하여 GCM 산출물의 시간 분해를 수행하였다. 본 연구에서는 MRC (Multiplicative Random Cascade) 모형을 기후변화 시나리오 기상자료에 적용함으로써 강우 자료의 시간 분해를 수행하고, 시간 분해 결과물을 활용하여 농촌 유역의 미래 홍수 확률을 산정해보고자 하였다. 본 연구의 결과는 향후 농촌 유역의 홍수 확률 산정 기법에 관한 기초 자료로 활용될 수 있을 것으로 사료된다.

• 대한토목학회논문집
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• 제28권6B호
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• pp.665-673
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• 2008

본 연구에서는 SWAT(Soil and Water Assessment Tool) 모형을 이용하여 (I)연구에서 기구축된 현재의 유역 수문환경 조건을 활용하여 미래수문환경에 따른 유역 수문-수질 변화 분석하는데 목적이 있다. 미래의 수문자료 중 기후변화 시나리오는 Downscaling된 GCMs 자료를 이용하였으며, 미래 토지이용 시나리오는 미래 토지이용 예측 기법인 개선된 CA-Markov 기법으로 분석하여 구축하였다. 그 결과 미래의 토지이용 시나리오에 따라 주거지와 나지 등의 도시지역의 꾸준한 증가와 산림과 농경지 등의 감소가 예측되었고 미래의 기후변화 경우 강우의 변동성이 커 현재보다 더욱 더 기후변화의 대응전략이 필요할 것으로 판단된다. 미래 토지이용 시나리오와 기후변화 시나리오를 결합한 미래의 수문환경을 30-40년 주기로 나누어 장기적인 미래의 특정기간에 대한 자료를 가지고 수문 변화 분석을 실시한 결과, 강우에 의해 양상이 변할 수 있으며 그에 따라 영양물질의 부하량 또한 Sediment는 120%, T-N은 16%, T-P는 10% 증가할 수 있는 것으로 분석되었다. 따라서 수문환경의 전반적인 변화에 따른 유출 영향의 평가가 필요하다고 판단되고 향후 수자원 분배와 물자원 관리와 기후변화 적응전략을 수립하는데 있어 본 연구가 도움이 될 것이라고 판단한다.