• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.2
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• pp.107-113
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• 2011

To evaluate the performance of the hydrodynamic model, we introduced 10 skill assessments that are assorted by two groups: quantitative skill assessments (Absolute Average Error or AAE, Root Mean Squared Error or RMSE, Relative Absolute Average Error or RAAE, Percentage Model Error or PME) and qualitative skill assessments (Correlation Coefficient or CC, Reliability Index or RI, Index of Agreement or IA, Modeling Efficiency or MEF, Cost Function or CF, Coefficient of Residual Mass or CRM). These skill assessments were applied and calculated to evaluate the hydrodynamic modeling at one of Florida estuaries for water level, current, and salinity as comparing measured and simulated values. We found that AAE, RMSE, RAAE, CC, IA, MEF, CF, and CRM are suitable for the error assessment of water level and current, and AAE, RMSE, RAAE, PME, CC, RI, IA, CF, and CRM are good at the salinity error assessment. Quantitative and qualitative skill assessments showed the similar trend in terms of the classification for good and bad performance of model. Furthermore, this paper suggested the criteria of the "good" model performance for water level, current, and salinity. The criteria are RAAE < 10%, CC > 0.95, IA > 0.98, MEF > 0.93, CF < 0.21 for water level, RAAE < 20%, CC > 0.7, IA > 0.8, MEF > 0.5, CF < 0.5 for current, and RAAE < 10%, PME < 10%, CC > 0.9, RI < 1.15, CF < 0.1 for salinity.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.823-832
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• 2007

In this study, the separation of short and long durations for estimation the parameters of IDF curve is suggested by using Multi-Objective Genetic Algorithm (MOGA). Objective functions are to minimize root mean squared error (RMSE) and relative RMSE between observed and computed values. The criteria for separation are two; the first one is to estimate more precisely the parameters of IDF curve and the second is to make a single IDF curve without non-continuous duration point. For this purpose 22 rainfall recording gauges operated by Korea Meteorological Administration are selected and three IDF curves that are used generally in South Korea are tested. The result shows that the IDF curve developed by Heo et al. (1999) would be the best of three tested IDF curves, and the suggested parameter estimation method using MOGA can compute more reliable parameters compared with empirical regression analysis.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.527-544
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• 2008

Generalized Pareto distribution (GPD) is frequently applied in hydrologic extreme value analysis. The main objective of statistics of extremes is the prediction of rare events, and the primary problem has been the estimation of the threshold and the exceedances which were difficult without an accurate method of calculation. In this paper, to obtain the threshold or the exceedances, four methods were considered. For this comparison a GPD model was used to estimate parameters and quantiles for the seven durations (1, 2, 3, 6, 12, 18 and 24 hours) and the ten return periods (2, 3, 5, 10, 20, 30, 50, 70, 80 and 100 years). The parameters and quantiles of the three-parameter generalized Pareto distribution were estimated with three methods (MOM, ML and PWM). To estimate the degree of fit, three methods (K-S, CVM and A-D test) were performed and the relative root mean squared error (RRMSE) was calculated for a Monte Carlo generated sample. Then the performance of these methods were compared with the objective of identifying the best method from their number.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Journal of Radiation Protection and Research
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• v.42 no.2
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• pp.83-90
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• 2017

Background: Different cases exist in the measurement of thyroid radiobioassays owing to the individual characteristics of the subjects, especially the potential variation in the counting efficiency. An In situ Object Counting System (ISOCS) was developed to perform an efficiency calibration based on the Monte Carlo calculation, as an alternative to conventional calibration methods. The purpose of this study is to evaluate the applicability of ISOCS to thyroid radiobioassays by comparison with a conventional thyroid monitoring system. Materials and Methods: The efficiency calibration of a portable high-purity germanium (HPGe) detector was performed using ISOCS software. In contrast, the conventional efficiency calibration, which needed a radioactive material, was applied to a scintillator-based thyroid monitor. Four radioiodine samples that contained $^{125}I$ and $^{131}I$ in both aqueous solution and gel forms were measured to evaluate radioactivity in the thyroid. ANSI/HPS N13.30 performance criteria, which included the relative bias, relative precision, and root-mean-squared error, were applied to evaluate the performance of the measurement system. Results and Discussion: The portable HPGe detector could measure both radioiodines with ISOCS but the thyroid monitor could not measure $^{125}I$ because of the limited energy resolution of the NaI(Tl) scintillator. The $^{131}I$ results from both detectors agreed to within 5% with the certified results. Moreover, the $^{125}I$ results from the portable HPGe detector agreed to within 10% with the certified results. All measurement results complied with the ANSI/HPS N13.30 performance criteria. Conclusion: The results of the intercomparison program indicated the feasibility of applying ISOCS software to direct thyroid radiobioassays. The portable HPGe detector with ISOCS software can provide the convenience of efficiency calibration and higher energy resolution for identifying photopeaks, compared with a conventional thyroid monitor with a NaI(Tl) scintillator. The application of ISOCS software in a radiation emergency can improve the response in terms of internal contamination monitoring.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.811-821
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• 2007

The intensity-duration-frequency (IDF) curves by Talbot, Sherman and Japanese type formulas are widely used in South Korea since the parameters are easily estimated. However, these IDF curves' accuracies are relatively worse than those of the IDF curves developed by Lee et al. (1993) and Heo et al. (1999), and different parameters for the given return periods should be computed. In this study, parameter estimation method for the IDF curve by Heo et al. (1999) is suggested using genetic algorithm (GA). Quantiles computed by at-site frequency analysis using the rainfall data of 22 rainfall gauges operated by Korea Meteorological Administration are employed to estimate the parameters of IDF curves and minimizing root mean squared error (RMSE) and relative RMSE (RRMSE) of observed and computed quantiles are used as objective functions of GA. The comparison of parameter estimation methods between the empirical regression analysis and the suggested method show that the IDF curve in which the parameters are estimated by GA using RRMSE as an objective function is superior to the IDF curves using RMSE.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.363-374
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• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.