• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.3-13
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• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.171-171
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• 2011

상수도의 합리적인 운용과 관리를 위해서는 급수량 예측이 매우 중요하다. 기존 급수량 예측은 신경망과 칼만 필터법을 사용한 연구들이 대부분이었다. 이러한 연구결과들은 높은 상관결과를 갖고 있지만 이는 자기상관계수에 대한 높은 의존도에 따른 결과로 볼 수 있다. 즉, 예측의 결과가 전날 수요량을 거의 그대로 따라오는 경향을 띄어, 급수량 예측 그래프가 기존 그래프를 오른쪽으로 이동시킨 것과 같이 나타난다. 본 연구에서는 이러한 문제점들을 해결하기 위해서 물수요량을 예측하는데 있어서 효과적인 예측인자를 도출하는 것이 우선되어야 할 것으로 판단되었다. 이에, 물수요량 특성을 효과적으로 나타내어 줄 수 있는 예측인자로서 강수량, 최저온도, 최고온도, 평균온도 등을 1차적으로 선정하였다. 이들 예측인자들과 서울시 물수요량과의 상관성을 평가하여 최적의 예측인자 Set과 지체시간 등을 산정하였다. 이렇게 선정된 예측인자와 Bayesian 통계기법 기반의 회귀분석 모형을 구축하여 물수요량을 예측하였다. 본 연구에서 적용하고자 하는 계층적 Bayesian 모형은 유사한 특성을 가지는 자료계열들 사이에서 서로 보완이 될 수 있는 정보들을 추출함으로써 모형이 갖는 불확실성을 상당히 줄일 수 있는 방법이다. 이러한 모형적 특징은 생산량 예측에 대한 불확실성 저감 측면에서 장점이 있을 것으로 판단된다. 본 연구에서는 광암, 암사, 구의, 뚝도, 영등포, 강북 정수장을 대상으로 모형의 적합성을 평가하였다. 이러한 연구결과는 향후 정수장 운영계획 및 동일한 시스템을 갖는 상수도 급수량 예측 시 유용하게 사용할 수 있을 것이다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.4
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• pp.357-374
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• 2012

Considerable uncertainties are included in ground properties used for tunnel designs due to the limited investigation and tests. In this study, a back analysis was performed to find optimal ground properties based on artificial neural network using both face mapping data and convergence measurement data. First of all, the rock class of a study tunnel is determined from face mapping data. Then the possible ranges of ground properties were selected for each rock class through a literature review on the previous studies and utilized to establish more precise learning data. To find an optimal training model, a sensitivity analysis was also conducted by varying the number of hidden layers and the number of nodes more minutely than the previous study. As a result of this study, more accurate ground properties could be obtained. Therefore it was confirmed that the accuracy of the results could be increased by making use of not only convergence measurement data but also face mapping data in tunnel back analyses using artificial neural network. In future, it is expected that the methodology suggested in this study can be used to estimate ground properties more precisely.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.563-563
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• 2016

국내 강우발생은 기상학적인 영향으로 인하여 장마기간(6~8월)에 집중되어있으며, 최근에는 기후변화의 영향으로 짧은 시간에 많은 양의 강우가 발생하는 집중호우의 발생빈도가 증가하고 있다. 또한, 시간과 지역에 관계없이 국지성호우의 발생빈도 역시 높아지고 있다. 집중호우와 국지성호우는 짧은 시간에 하천수위를 상승시키므로 홍수로 인한 물적 피해가 크게 발생된다. 국토교통부에서는 그동안 홍수예보에 필수적인 우량, 하천수위 등 기초자료를 확보하기 위해 관측소(500여개) 및 홍수량 측정지점(80여개)을 확대하였으며, 관측된 자료는 모두 전산망에 기록, 보관하고 있다. 또한 한강, 금강, 낙동강, 영산강의 경우 홍수통제소에서 홍수량 예측 계산 등을 통해 홍수 예경보를 실시하고 있다. 하지만 4대강을 제외한 중소하천의 홍수예경보에 대한 정보를 찾아볼 수 없으며, 현재 연구가 진행중이다. 강우-유출모형을 활용하여 중소하천의 강우와 유출의 관계를 해석하는 과정은 다양한 인자를 고려해야하지만 중소하천의 경우 하천단면 등 하천자료가 충분히 구축되어 있지 못하므로 유출량 계산에 많은 어려움을 겪고 있다. 이에 본 연구에서는 중소하천의 홍수위 예측을 위해 한강의 과거 수위와 현재 수위만을 활용하여 인공신경망(Artificial Neural Network, ANN)의 학습을 진행하였다. 첫 번째로 ANN을 활용하여 한강유역 중 홍수예보지점(잠수교)의 수위변화에 직접적으로 연관이 있는 5개 수위관측소를 선정하였으며, 과거 장마기간(6~8월)관측 자료를 활용하였다. 두 번째로 홍수예보지점(잠수교)과 5개 수위관측소의 과거 관측수위(2009~2014년)를 인공신경망의 학습자료로 활용하여 모델을 훈련시켰으며, 마지막으로 2015년의 관측수위를 이용하여 ANN의 학습정확도에 대한 검증을 하였다. 본 과정은 수위예측을 위한 ANN의 훈련단계로 Training/Test를 반복하였으며, 학습결과와 2015년 관측수위 비교시 $R^2=0.987$과 상관계수 r=0.994로 유사한 패턴을 보였으나 최대치와 최소치에 대한 오차가 있음을 확인하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.1
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• pp.37-53
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• 2012

There is considerable uncertainty in ground properties used in tunnel designs. In this study, a back analysis was performed to find optimal ground properties based on the artificial neural network facility of MATLAB program of using tunnel monitoring data. Total 81 data were constructed by changing elastic modulus and coefficient of lateral pressure which have great influence on tunnel convergence. A sensitivity analysis was conducted to establish an optimal training model by varying the number of hidden layers, the number of nodes, learning rate, and momentum. Meanwhile, the optimal training model was selected by comparing MSE (Mean Squared Error) and coefficient of determination ($R^2$) and was used to find the correct elastic moduli of layers and the coefficient of lateral pressure. In future, it is expected that the suggested method of this study can be applied to determine the optimum tunnel support pattern under given ground conditions.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1249-1263
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• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.29-37
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• 2008

In this parer, we purpose for applying the RFID(Radio Frequency IDentification) system in National Road Traffic Volume Survey. Because there is limitation for shipping RFID Tag on every car, we firstly defined Expansion (process of making the number of all cars which passed survey point from sampling data) and determined the best methodology among 3 methodologies (Time factor Model, Fuzzy Model, Artificial Neural Network). As a result of analysis, Time Factor Model was chosen as the best methodology for Expansion. Also, we analyzed to find an application of the RFID system in National Road Traffic Volume Survey and obtained a possibility applying it. It is expected that if the RFID system is used in Traffic Volume Survey, the survey cost is saved than before.