• Title/Summary/Keyword: validation studies

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Estimation of river discharge using satellite-derived flow signals and artificial neural network model: application to imjin river (Satellite-derived flow 시그널 및 인공신경망 모형을 활용한 임진강 유역 유출량 산정)

  • Li, Li;Kim, Hyunglok;Jun, Kyungsoo;Choi, Minha
    • Journal of Korea Water Resources Association
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    • v.49 no.7
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    • pp.589-597
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    • 2016
  • In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

Oceanic Application of Satellite Synthetic Aperture Radar - Focused on Sea Surface Wind Retrieval - (인공위성 합성개구레이더 영상 자료의 해양 활용 - 해상풍 산출을 중심으로 -)

  • Jang, Jae-Cheol;Park, Kyung-Ae
    • Journal of the Korean earth science society
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    • v.40 no.5
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    • pp.447-463
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    • 2019
  • Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.

Kriging of Daily PM10 Concentration from the Air Korea Stations Nationwide and the Accuracy Assessment (베리오그램 최적화 기반의 정규크리깅을 이용한 전국 에어코리아 PM10 자료의 일평균 격자지도화 및 내삽정확도 검증)

  • Jeong, Yemin;Cho, Subin;Youn, Youjeong;Kim, Seoyeon;Kim, Geunah;Kang, Jonggu;Lee, Dalgeun;Chung, Euk;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.379-394
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    • 2021
  • Air pollution data in South Korea is provided on a real-time basis by Air Korea stations since 2005. Previous studies have shown the feasibility of gridding air pollution data, but they were confined to a few cities. This paper examines the creation of nationwide gridded maps for PM10 concentration using 333 Air Korea stations with variogram optimization and ordinary kriging. The accuracy of the spatial interpolation was evaluated by various sampling schemes to avoid a too dense or too sparse distribution of the validation points. Using the 114,745 matchups, a four-round blind test was conducted by extracting random validation points for every 365 days in 2019. The overall accuracy was stably high with the MAE of 5.697 ㎍/m3 and the CC of 0.947. Approximately 1,500 cases for high PM10 concentration also showed a result with the MAE of about 12 ㎍/m3 and the CC over 0.87, which means that the proposed method was effective and applicable to various situations. The gridded maps for daily PM10 concentration at the resolution of 0.05° also showed a reasonable spatial distribution, which can be used as an input variable for a gridded prediction of tomorrow's PM10 concentration.

Development and Validation of the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC) (한국형 긍정적 행동지원 3차 실행충실도 척도(KT3-FC)의 개발과 타당화)

  • Won, Sung-Doo;Chang, Eun Jin;Cho Blair, Kwang-Sun;Song, Wonyoung;Nam, Dong Mi
    • Korean Journal of School Psychology
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    • v.17 no.2
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    • pp.165-180
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    • 2020
  • As a tiered system of supports, School-Wide Positive Behavior Support (SWPBS) is an evidence-based practice in the educational system of Korea. An important aspect of SWPBS is the ongoing progress monitoring and evaluation of implementation fidelity. This study aimed to develop and validate the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC). The preliminary KT3-FC consisted of a 37-item, 6-factor checklist. In the first phase of the study, 10 experts reported that the range of content validity of the KT3-FC was adequate. In the second phase of the study, 185 teachers (52 men and 133 women) who implemented SWPBS completed the KT3-FC, Individualized Supports Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. An exploratory factor analysis resulted in a 5-factor structure, with 20 items, instead of 37 items, consisting of: (a) progress monitoring and evaluation of the individualized supports, (b) provision of supports by aligning and integrating mental health and SWPBS, (c) crisis management planning, (d) problem behavior assessment, and (e) establishment of individualized support team. The internal consistency of the KT3-FC was good (full scale α = .950, sub-factor α = .888 ~ .954). In addition, the KT3-FC showed good convergent validity, having statistically significant correlations with the Individualized Support Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and the PBS Effectiveness Scale. Finally, the confirmatory factor analysis showed that the 5-factor model of the KT3-FC had some good model fits, indicating that the newly developed fidelity measure could be a reliable and valid tool to assess the implementation of Tier 3 supports in Korean schools. Accordingly, the KT3-FC could contribute to implement SWPBS as an evidence-based behavioral intervention for Korean students.

Satellite-Based Cabbage and Radish Yield Prediction Using Deep Learning in Kangwon-do (딥러닝을 활용한 위성영상 기반의 강원도 지역의 배추와 무 수확량 예측)

  • Hyebin Park;Yejin Lee;Seonyoung Park
    • Korean Journal of Remote Sensing
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    • v.39 no.5_3
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    • pp.1031-1042
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    • 2023
  • In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

Analysis of the Impact of Satellite Remote Sensing Information on the Prediction Performance of Ungauged Basin Stream Flow Using Data-driven Models (인공위성 원격 탐사 정보가 자료 기반 모형의 미계측 유역 하천유출 예측성능에 미치는 영향 분석)

  • Seo, Jiyu;Jung, Haeun;Won, Jeongeun;Choi, Sijung;Kim, Sangdan
    • Journal of Wetlands Research
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    • v.26 no.2
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    • pp.147-159
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    • 2024
  • Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

The Cutoff Value of HbA1c in Predicting Diabetes and Impaired Fasting Glucose (당뇨병 및 공복혈당장애 예측을 위한 당화혈색소 값)

  • Kwon, Seyoung;Na, Youngak
    • Korean Journal of Clinical Laboratory Science
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    • v.49 no.2
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    • pp.114-120
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    • 2017
  • There have been many studies to develop methods for predicting diabetes and to prevent diabetes. The validity of glycated hemoglobin (HbA1c), one of the commonly known tools in predicting diabetes, has been verified by many previous studies. In this study, we examined the cutoff value of HbA1c for diabetes and impaired fasting glucose (IFG). Based on this study, we proposed a proper clinical guideline and evaluated the validation of the guideline. Excluding those without blood glucose and HbA1c data, we used the data of 5,161 subjects (2,281 men and 2,880 women) over the age of 20 years from the 2015 Korean National Health and Nutrition Examination Survey. The correlation efficient of fasting plasma glucose (FPG) and HbA1c was 0.79, indicating a strong relationship. Howeve, the correlation efficient of FPG and HbA1c was low, showing 0.27 in non-diabetes, 0.39 in IFG, and 0.66 in diabetes, showing a strong relationship. The cutoff value of HbA1c for predicting diabetes using ROC curve was 6.05% (sensitivity 84.6%, and specificity 92.0%), and AUC was 0.941 (0.937 in men, and 0.946 in women). The cutoff value of HbA1c for predicting IFG using ROC curve was 5.55% (sensitivity 64.5%, and specificity 70.0%), and AUC was 0.733 (0.708 in men, and 0.764 in women). Therefore, it may not be appropriate to apply the guidelines for diagnosing IFG since sensitivity and specificity were below 70%. For future studies retarding the cutoff value of HbA1c in predicting IFG, high sensitivity and specificity are expected if we segment the reference range of IFG.

An Longitudinal Analysis of Changing Beliefs on the Use in IT Educatee by Elaboration Likelihood Model (정교화 가능성 모형에 의한 IT 피교육자 신용 믿음 변화의 종단분석)

  • Lee, Woong-Kyu
    • Asia pacific journal of information systems
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    • v.18 no.3
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    • pp.147-165
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    • 2008
  • IT education can be summarized as persuading the educatee to accept IT. The persuasion is made by delivering the messages for how-to-use and where-to-use to the educatee, which leads formulation of a belief structure for using IT. Therefore, message based persuasion theory, as well as IT acceptance theories such as technology acceptance model(TAM), would play a very important role for explaining IT education. According to elaboration likelihood model(ELM) that has been considered as one of the most influential persuasion theories, people change attitude or perception by two routes, central route and peripheral route. In central route, people would think critically about issue-related arguments in an informational message. In peripheral route, subjects rely on cues regarding the target behavior with less cognitive efforts. Moreover, such persuasion process is not a one-shot program but continuous repetition with feedbacks, which leads to changing a belief structure for using IT. An educatee would get more knowledge and experiences of using IT as following an education program, and be more dependent on a central route than a peripheral route. Such change would reformulate a belief structure which is different from the intial one. The objectives of this study are the following two: First, an identification of the relationship between ELM and belief structures for using IT. Especially, we analyze the effects of message interpretation through both of central and peripheral routes on perceived usefulness which is an important explaining variable in TAM and perceived use control which have perceived ease of use and perceived controllability as sub-dimensions. Second, a longitudinal analysis of the above effects. In other words, change of the relationship between interpretation of message delivered by IT education and beliefs of IT using is analyzed longitudinally. For achievement of our objectives, we suggest a research model, which is constructed as three-layered. While first layer has a dependent variable, use intention, second one has perceived usefulness and perceived use control that has two sub-concepts, perceived ease of use and perceived controllability. Finally, third one is related with two routes in ELM, source credibility and argument quality which are operationalization of peripheral route and central route respectively. By these variables, we suggest five hypotheses. In addition to relationship among variables, we suggest two additional hypotheses, moderation effects of time in the relationships between perceived usefulness and two routes. That is, source credibility's influence on perceived usefulness is decreased as time flows, and argument quality's influence is increased. For validation of it, our research model is tested empirically. With measurements which have been validated in the other studies, we survey students in an Excel class two times for longitudinal analysis. Data Analysis is done by partial least square(PLS), which is known as an appropriate approach for multi-group comparison analysis with a small sized sample as like this study. In result. all hypotheses are statistically supported. One of theoretical contributions in this study is an analysis of IT education based on ELM and TAM which are considered as important theories in psychology and IS theories respectively. A longitudinal analysis by comparison between two surveys based on PLS is also considered as a methodological contribution. In practice, finding the importance of peripheral route in early stage of IT education should be notable.

Validity and Reliability of CAT and Dyspnea-12 in Bronchiectasis and Tuberculous Destroyed Lung

  • Lee, Bo-Young;Lee, Seo-Hyun;Lee, Jae-Seung;Song, Jin-Woo;Lee, Sang-Do;Jang, Seung-Hun;Jung, Ki-Suck;Hwang, Yong-Il;Oh, Yeon-Mok
    • Tuberculosis and Respiratory Diseases
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    • v.72 no.6
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    • pp.467-474
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    • 2012
  • Background: The objective of this study was to assess the validity and reliability of the Korean version of chronic obstructive pulmonary disease assessment test (CAT) and Dyspnea-12 Questionnaire for patients with bronchiectasis or tuberculous destroyed lung. Methods: For 62 bronchiectasis patients and 37 tuberculous destroyed lung patients, 3 questionnaires including St. George's Respiratory Questionnaires (SGRQ), CAT, and Dyspnea-12 were obtained, in addition to spirometric measurements. To assess the validity of CAT and Dyspnea-12, correlation with SGRQ was evaluated. To assess the reliability of CAT and Dyspnea-12, Cronbach's ${\alpha}$ coefficient was calculated. Results: The mean ages of the patients were $60.7{\pm}8.3$ years in bronchiectasis and $64.4{\pm}9.3$ years in tuberculous destroyed lung. 46.8% and 54.1% were male, respectively. The SGRQ score was correlated with the score of the Korean version of CAT (r=0.72, p<0.0001) and Dyspnea-12 (r=0.67, p<0.0001) in bronchiectasis patients. The SGRQ score was correlated with the score of CAT (r=0.86, p<0.0001) and Dyspnea-12 (r=0.80, p<0.0001) in tuberculous destroyed lung patients. The Cronbach's ${\alpha}$ coefficient for the CAT and Dyspnea-12 were 0.84 and 0.90 in bronchiectasis, and 0.88 and 0.94 in tuberculous destroyed lung, respectively. Conclusion: We found that Korean version of CAT and Dyspnea-12 are valid and reliable in patients with tuberculous destroyed lung and bronchiectasis.

Building Wind Corridor Network Using Roughness Length (거칠기길이를 이용한 바람통로 네트워크 구축)

  • An, Seung Man;Lee, Kyoo-Seock;Yi, Chaeyeon
    • Journal of the Korean Institute of Landscape Architecture
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    • v.43 no.3
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    • pp.101-113
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    • 2015
  • The purpose of this study is increasing ventilation network usability for urban green space planning by enhancing its practicality and detail. A ventilation network feature extraction technique using roughness length($z_0$) was proposed. Continuously surfaced DZoMs generated from $z_0$(cadastral unit) using three interpolations(IDW, Spline, and Kriging) were compared to choose the most suitable interpolation method. Ventilation network features were extracted using the most suitable interpolation technique and studied with land cover and land surface temperature by spatial overlay comparison. Results show Kriging is most suitable for DZoM and feature extraction in comparison with IDW and Spline. Kriging based features are well fit to the land surface temperature(Landsat-7 ETM+) on summer and winter nights. Noteworthy is that the produced ventilation network appears to mitigate urban heat loads at night. The practical use of proposed ventilation network features are highly expected for urban green space planning, though strict validation and enhancement should follow. (1) $z_0$ enhancement, (2) additional ventilation network interpretation and editing, (3) linking disconnected ventilation network features, and (4) associated dataset enhancement with data integrity should technically preceded to enhance the applicability of a ventilation network for green space planning. The study domain will be expanded to the Seoul metropolitan area to apply the proposed ventilation network to green space planning practice.