• 상하수도학회지
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• 제31권5호
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

• 한국항행학회논문지
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• 제27권1호
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• pp.133-138
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• 2023

최근 급격한 기후 변화 및 온난화로 인한 부작용으로 전 세계적으로 홍수 재해의 빈도 및 피해 규모가 증가하고 있다. 국내의 경우, 한강 수위는 대한민국 수도인 서울의 홍수 재해를 예방하기 위한 주요 관리 대상이다. 본 논문에서는 기계학습 기반의 한강 수위 예측을 개선하기 위해 관련 데이터 품질을 종합적으로 진단하고 이를 개선하기 위한 전처리 방안을 제안한다. 구체적으로는 결측치 처리와 교차 상관 분석을 통해 데이터를 완전성, 유효성, 그리고 정확성 측면에서 개선한다. 또한, 제안한 데이터 개선 방법이 한강 수위 예측 성능에 미치는 영향을 분석하기 위해 랜덤 포레스트와 LightGBM을 이용하여 성능 평가를 수행한다.

• 한국습지학회지
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• 제22권2호
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• pp.106-112
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• 2020

습지는 수문, 환경, 생태학적으로 중요한 기능 및 역할을 하며, 특히 습지 내의 수위는 습지의 기능과 환경 등 다양한 분석을 위해 필수적인 자료이다. 그러나 습지는 수위자료를 측정하지 않는 미계측 지역이 많기 때문에, 수위 예측에 대한 연구는 매우 미흡한 실정이다. 따라서 본 연구에서는 습지의 수위를 예측하기 위해 다중회귀분석, 주성분회귀분석, 인공신경망, DNN을 활용하여 수위 예측모형을 개발하였다. 대상지역으로 경상남도 양산시에 위치한 금정산 산지습지를 선정하였고, 2017년 4월부터 2018년 7월까지의 수위 측정자료를 종속변수로 사용하였다. 수문자료와 기상자료를 독립변수로 사용하였다. 예측력 평가결과 최종 모형으로 선정된 DNN을 활용한 수위 예측모형의 예측력 평가결과 RMSE는 6.359, NRMSE는 18.91%로 비교적 산지습지의 수위를 잘 예측하는 것으로 나타났다. 본 연구결과를 활용한다면 기존의 미비하였던 미계측 지점의 수위를 활용한 습지유지 및 관리 기법 개발에 기초자료로 사용할 수 있을 것으로 판단된다.

• 한국농공학회논문집
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• 제66권1호
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Ecology and Resilient Infrastructure
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• 제7권4호
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• pp.345-352
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• 2020

최근 하천범람에 따른 피해를 최소화하기 위해서는 대피를 위한 선행시간을 확보하는 것이 매우 중요하다. 본 연구에서는 현재 하천에서 측정되고 있는 수위 관측 자료를 이용하여 이상호우 발생시 하류의 수위를 예측하였다. 수위 예측을 위해 다중회귀모형 및 인공신경망 모형을 섬강시험유역에 적용하였다. 다중회귀모형 및 인공신경망 모형의 학습에는 섬강시험유역의 2002년부터 2010년까지의 수위 관측 자료를 이용하였으며, 학습된 모형을 이용하여 발생 가능한 수위를 예측하였다. 모의 결과 인공신경망 수위예측모형의 결정계수는 0.991 - 0.999로 나타났으며, 다중회귀수위예측 모형의 결정계수는 0.945 - 0.990로 나타나 인공신경망을 이용한 수위예측모형이 다중회귀모형보다 좀 더 나은 예측 결과를 나타내는 것을 확인할 수 있었다. 본 연구결과는 향후 하천에서 선행시간을 확보한 홍수 예보 구축에 활용할 수 있을 것으로 판단된다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제19권4호
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• pp.23-30
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• 2014

Coastal aquifer in Muan, Jeonnam, has experienced heavy seawater intrusion caused by the extraction of a substantial amount of groundwater for the agricultural purpose throughout the year. It was observed that groundwater level dropped below sea level due to heavy pumping during a dry season, which could accelerate seawater intrusion. Therefore, water level needs to be monitored and managed to prevent further seawater intrusion. The purpose of this study is to develop the autoregressive-cross-regressive (ARCR) models that can predict the present or future groundwater level using its own previous values and pumping events. The ARCR model with pumping and water level data of the proceeding five hours (i.e., the model order of five) predicted groundwater level better than that of the model orders of ten and twenty. This was contrary to expectation that higher orders do increase the coefficient of determination ($R^2$) as a measure of the model's goodness. It was found that the ARCR model with order five was found to make a good prediction of next 48 hour groundwater levels after the start of pumping with $R^2$ higher than 0.9.

• 대한토목학회논문집
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• 제37권6호
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• pp.981-987
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• 2017

투수성이 큰 화산섬인 제주도에서는 땅속으로 함양된 지하수자원이 가장 중요한 수원이므로 지하수의 적정관리가 매우 중요하다. 특히 가뭄시 지하수의 이용은 염수침투를 유발할 수 있으므로 지하수위 강하에 따른 단계별 제한 조치가 마련되어 있다. 농업용 지하수위에 대한 적정 지하수 이용을 위해서는 보다 장기적인 예측을 통해 사전에 대비하는 것이 필요하다. 이에 본 연구에서는 인공신경망 모형을 이용한 지하수위의 월단위예측기법을 개발하였고, 대표적인 관측공에 대해 적용하였다. 월단위 지하수위를 예측한 결과 학습 및 검증기간 모두 예측 성능이 우수한 것으로 분석되었다. 또한 장기예측을 위해서 입력인자로 월단위 지하수위 예측치를 순차적으로 이용하는 연속지하수위예측 모형을 구축하고 수개월 동안 무강수의 극한조건에 대한 지하수위 저하 양상을 분석하였다.

• 한국농공학회논문집
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• 제63권6호
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• pp.131-140
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• 2021

It is becoming more important to utilize reclaimed lands in South Korea, due to the increasing competition for its usage among different sectors. However, the high groundwater level and poor permeability are exposing them to deterioration by salinization. Salinization is difficult to predict because the pattern changes according to various characteristics of soil and groundwater. In this study, the capillary rising time was studied by the water content profile in the soil. The prediction equation of soil salinity was developed based on simulation result of the CHEMFLO model. to enable prediction considering various soil water content and groundwater level. The two terms constituting the equation showed the coefficients of determination of 0.9816 and 0.9824, respectively. Using the prediction equation of the study, the surface salinity can be easily predicted from the initial surface salinity and the salinity of the groundwater. In the future, more precise predictions will be possible with the results of studies on the hydraulic characteristics of various reclaimed soils, changes in water content profile by seasonal and climate events.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권3호
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• 소음진동
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• 제3권2호
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• pp.155-161
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• 1993

The vibration generated by the machinery on board is transmitted to the hull and into the water. At the early design stage, the prediction of the hull vibration and the radiated noise level is very important to reduce their levels. In this study, SAE(Statistical Energy Analysis) technique is applied to predict structureborne noise level of the hull considering fluid loading. Rayleigh integral is applied to predict the radiated noise level. The results of comparision between the predictions and measurements for the reinforced cylindrical shell have shown good agreements.