• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Industry Promotion Research
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• v.8 no.4
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• pp.47-60
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• 2023

The purpose of this study is to examine the causal relationship between self-efficacy and safety consciousness of health belief factors and how they affect the disaster prevention behavior of construction workers using TBM. To this end, a research model is presented that applies the main variables of the Health Belief Theory, a social psychological health behavior change model developed to predict and explain health-related behaviors. To empirically verify the research model of this study, a survey was conducted among construction workers who have experience in using TBMs for chemical plant construction. The results showed that, first, the perceived severity of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness; second, the perceived probability of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness. Third, the perceived obstacles of construction workers utilizing chemical plant construction have a significant effect on self-efficacy and safety consciousness. Fourth, the perceived benefits of construction workers utilizing chemical plant construction were found to have a significant effect on self-efficacy and safety awareness. The purpose of this study is to reduce critical accidents through disaster prevention behavior of chemical plant construction workers through TBM.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.887-896
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• 2018

The purpose of this preliminary study was to investigate the effect of fall prevention program on the occurrence of falls in inpatients at general hospital. Before the intervention, we identified the occurrence of the rate of falls in I hospital at Incheon and 190 patients received rehabilitation treatment. The causes of falls were carelessness of caregivers and therapists, increased burden of therapists, and height of beds. After recognizing the problems, the authors developed a fall prevention program through the PDCA model. The fall prevention program consisted of a video of fall prevention education and education for the patients and caregivers, environmental improvement and education of therapists for two months. After intervention, 230 patients were subjected to be included as analysis of the incidence of falls included As a result, the fall incidence was reduced by 34.1~66.7% in the pain clinic and 21.3~40.8% in the exercise clinic / department of occupational therapy compared to the before intervention. These findings show that the fall prevention program has a positive effect on the fall of inpatients, and it can be used as a model for fall prevention.

• Journal of Korean Academy of Nursing
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• v.39 no.4
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• pp.490-498
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• 2009

Purpose: This study was done to identify the effects of a Human Papillomavirus (HPV) linked to cervix cancer prevention education program for unmarried university female students. A new model in the cervix cancer prevention is provided. Methods: The research design was a nonequivalent control group pretest-posttest design. Participants were 63 female students in one of two university in an experimental group (29 students) and control group (34 students). After 4 weeks education, the differences between the two groups in the measurement variables were compared. Twelve weeks later, a follow-up test was done for experimental group only. Results: After the education, experimental group showed significantly higher scores in all variables, the intention for Pap test (Z=-3.73, p<.001), intention for HPV vaccination (Z=-3.14, p=.002), general cancer prevention behavior (Z=-2.20, p=.028), attitudes to Pap (Z=-3.23, p=.001), benefits of cancer prevention behavior (Z=-3.97, p<.001), and HPV linked to cervix cancer knowledge (Z=-5.40, p<.001). In the follow-up study, the experimental group showed intermediate effects in intention for Pap test, intention of HPV vaccination and HPV linked to cervix cancer knowledge as well as short term effects in general cancer prevention behavior, attitudes to Pap and benefits of cancer prevention behavior. Conclusion: The program developed for this study on prevention education of HPV linked to cervix cancer was effective for unmarried university students in the short term and intermediate duration. Other educational approaches should be developed and short term effects and longitudinal changes of the education should be assessed. This education program should also be replicated for other female groups including unmarried working women or female adolescents.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7375-7379
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• 2015

A high prevalence of Opisthorchis viverrini infection is usually found in wetland geographical areas of Thailand where people have traditional behavior of eating uncooked freshwater fish dishes which results in cholangiocarcinoma (CCA) development. There were several approaches for reducing opisthorchiasis-linked CCA, but the prevalence remains high. To develop community participation as a suitable model for CCA prevention is, firstly, to know what factors are related. We therefore aimed to investigate factors associated with the community participation in CCA prevention among rural residents in wetland areas of Ubon Ratchathani, Thailand. This was a cross-sectional analytic study. All participants were 30-69 years of age, and only one member per house was invited to participate. A total of 906 participants were interviewed and asked to complete questionnaires. Independent variables were socio-demographic parameters, knowledge, health belief and behavior to prevent CCA. The dependent variable was community participation for CCA prevention. Descriptive statistics were computed as number, percentage, mean and standard deviation. Associations were assessed using logistic regression analysis with a P-value <0.05 considered statistically significant. Of all the participants, more than 60% had regularly participated in activities to prevent CCA following health officials advice. Age and health behavior to prevent CCA were factors associated with community participation for CCA (p<0.001). Both factors will be taken into consideration for community participation approaches for CCA prevention through participatory action research (PAR) in future studies.

• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.549-567
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• 2015

A first-order moment method (FORM) reliability analysis is commonly used for structural stability analysis. It requires the values and partial derivatives of the performance to function with respect to the random variables for the design. These calculations can be cumbersome when the performance functions are implicit. A Gaussian process (GP)-based response surface is adopted in this study to approximate the limit state function. By using a trained GP model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis with a FORM, thereby reducing the number of stability analysis calculations. This dynamic renewed knowledge source can provide great assistance in improving the predictive capacity of GP during the iterative process, particularly from the view of machine learning. An iterative algorithm is therefore proposed to improve the precision of GP approximation around the design point by constantly adding new design points to the initial training set. Examples are provided to illustrate the GP-based response surface for both structural and non-structural reliability analyses. The results show that the proposed approach is applicable to structural reliability analyses that involve implicit performance functions and structural response evaluations that entail time-consuming finite element analyses.

• Journal of the Korean Geotechnical Society
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• v.21 no.1
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• pp.69-80
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• 2005

In order to investigate the stabilizing effect of piles against sliding, a series of model tests were carried out. The model apparatus was designed to perform the model test of slope reinforced by stabilizing piles. The instrumentation system was used to measure the deflection of stabilizing piles during slope failure. The stabilizing effect of the piles in a row with some interval ratio is larger than the isolated pile without interval ratio. Because the prevention force of piles in a row increased due to the soil arching effect between piles during slope failure. Especially, the maximum value of prevention ratio was presented at 0.5 of interval ratio. If the required prevention ratio is 1.1, the interval ratio must be installed from 0.5 to 0.8. Also, the stabilizing effect of piles against sliding is excellent at the interval ratio between 0.5 and 0.8. This value can be proposed as the criterion of the interval ratio between piles against slope failure.