• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.2
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• pp.303-310
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• 2017

Evapotranspiration is a significant hydrologic quantity for understanding the amount of available water resource evaluation, water balance analysis, water circulation and energy circulation. Various methods have been developed for estimating the evapotranspiration using data observed at meteorological observatories. Especially, the focus of methods has been on the complementary relationship that the actual evapotranspiration is equal to the difference between the twice of evapotranspiration in the wet condition and the potential evapotranspiration. The Granger and Gary (GG) method is an empirical formula that can be used to estimate the evapotranspiration using only empirical parameters based on the complementary relationship and using only the net radiation and temperature of the region. In this study, we compared the evapotranspiration data observed at 10 sites in Asia within the dataset of FLUXNET2015, with the evapotranspiration calculated by GG method. The evapotranspiration in inland area was estimated more accurately than that of coastal area. Simulated Annealing (SA) was used for the coastal area to modify the parameters. Using the modified GG method, we could improve the statistics such as root mean square error, the coefficient of determination ($R^2$), and the mean absolute ${\mid}BIAS{\mid}$ of the evapotranspiration estimation in coastal area.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.642-650
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• 2008

A mathematical model was developed for predicting the growth rate of Enterobacter sakazakii in tryptic soy broth medium as a function of the combined effects of temperature (5, 10, 20, 30, and $40^{\circ}C$), pH (4, 5, 6, 7, 8, 9, and 10), and the NaCl concentration (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10%). With all experimental variables, the primary models showed a good fit ($R^2=0.8965$ to 0.9994) to a modified Gompertz equation to obtain growth rates. The secondary model was 'In specific growth $rate=-0.38116+(0.01281^*Temp)+(0.07993^*pH)+(0.00618^*NaCl)+(-0.00018^*Temp^2)+(-0.00551^*pH^2)+(-0.00093^*NaCl^2)+(0.00013^*Temp*pH)+(-0.00038^*Temp*NaCl)+(-0.00023^*pH^*NaCl)$'. This model is thought to be appropriate for predicting growth rates on the basis of a correlation coefficient (r) 0.9579, a coefficient of determination ($R^2$) 0.91, a mean square error 0.026, a bias factor 1.03, and an accuracy factor 1.13. Our secondary model provided reliable predictions of growth rates for E. sakazakii in broth with the combined effects of temperature, NaCl concentration, and pH.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.195-202
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• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• Geomechanics and Engineering
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• v.21 no.4
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• pp.391-399
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• 2020

Brittleness is one of the most important properties of rock which has a major impact not only on the failure process of intact rock but also on the response of rock mass to tunneling and mining projects. Due to the lack of a universally accepted definition of rock brittleness, a wide range of methods, including direct and indirect methods, have been developed for its measurement. Measuring rock brittleness by direct methods requires special equipment which may lead to financial inconveniences and is usually unavailable in most of rock mechanic laboratories. Accordingly, this study aimed to develop a new strength-based index for predicting rock brittleness based on the obtained base form. To this end, an innovative algorithm was developed in Matlab environment. The utilized algorithm finds the optimal index based on the open access dataset including the results of punch penetration test (PPT), uniaxial compressive and Brazilian tensile strength. Validation of proposed index was checked by the coefficient of determination (R²), the root mean square error (RMSE), and also the variance for account (VAF). The results indicated that among the different brittleness indices, the suggested equation is the most accurate one, since it has the optimal R², RMSE and VAF as 0.912, 3.47 and 89.8%, respectively. It could finally be concluded that, using the proposed brittleness index, rock brittleness can be reliably predicted with a high level of accuracy.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.206-216
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• 2017

This paper proposes a method to simultaneously estimate two degrees of freedom in wrist forces (extension - flexion, adduction - abduction) and one degree of freedom in grasping forces using Electromyography (EMG) signals of the forearms. To correlate the EMG signals with the forces, we applied a multi - layer perceptron(MLP), which is a machine learning method, and used the characteristics of the muscles constituting the forearm to generate learning data. Through the experiments, the similarity between the MLP target value and the estimated value was investigated by applying the coefficient of determination ($R^2$) and root mean square error (RMSE) to evaluate the performance of the proposed method. As a result, the $R^2$ values with respect to the wrist flexion-extension, adduction - abduction and grasping forces were 0.79, 0.73 and 0.78 and RMSE were 0.12, 0.17, 0.13 respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• KIEAE Journal
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• v.11 no.1
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• pp.15-20
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• 2011

Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design. For the six locations (Seoul, Incheon, Daejeon, Deagu, Gwangju and Busan) in South Korea where the global hourly solar irradiation (GHSI) is currently measured, GHSI was calculated using a comparatively simple cloud cover radiation model (CRM) and sunshine fraction radiation model (SFRM). The result was that the measured and calculated values of GHSI were similar for the six regions. Results of cloud cover and sunshine fraction models have been compared with the measured data using the coefficient of determination (R2), root-mean-square error (RMSE) and mean bias error (MBE). The strength of correlation R2 varied within similar ranges: 0.886-0.914 for CRM and 0.908-0.934 for SFRM. Average MBE for the CRM and SFRM were 6.67 and 14.02 W/m2, respectively, and average RMSE 104.36 and 92.15 W/m2. This showed that SFRM was slightly accurate and used many regions as compared to CRM for prediction of GHSI.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.65-74
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• 2012

In this study, three different unit hydrograph methods (NRCS, Snyder and Clark) in the HEC-HMS were compared to find better fit with the observed data in the Namgang-Dam watershed. The Sancheong, Shinan, and Changchon in Namgang-Dam watershed were selected as the study watersheds. The input data for HEC-HMS were calculated land use, digital elevation map, stream, and watershed map provided by WAter Management Information System (WAMIS). Sixty six storms from 2004 to 2011 were selected for model calibration and validation. Three unit hydrograph methods were compared with the observed data in terms of simulated runoff volume, and peak runoff for the selected storms. The results showed that the coefficient of determination ($R^2$) for the peak runoff was 0.8295~0.9999 and root mean square error (RMSE) was 0.029~0.086 mm/day for calibration stages. In the model validation, $R^2$ for the peak runoff was 0.9061~0.9916 and RMSE was 0.030~0.088 mm/day which were more accurate than calibrated data. Analysis of variance showed that there was no significant difference among the three unit hydrograph methods.

• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.79-86
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• 2024

As the frequency and intensity of heavy rains increase, the vulnerability of agriculture to disasters also increases. Consequently, there is a need to improve flood and inundation predictions. To enhance the accuracy of inundation predictions, it is essential to monitor water level and discharge data within agricultural areas. This study was conducted to monitor water levels and rainfall in the Cheongju Sindae area from 2022 to 2023, and the data was utilized as input and validation data for agricultural inundation modeling. Four irrigation drainage canals were installed to a square-shaped concrete structure where the water level gauge is. It was then confirmed that the water level rises with rainfall. The flow velocities were monitored during periods of heavy rainfall. The rating curve, which estimates water level and flow velocity based on observations, was estimated using the software K-HQ. The resulting curve was presented with the Coefficient of Determination (R²). K-HQ was also used to calculate the equation for the rating curve, taking outliers into account at each data point. Outliers were extracted and the rating curve was recalculated. As the coefficient of determination of three out of four stations exceeded 0.95, the estimated rating curve may be considered reliable for discharge estimation. This study provides critical data for enhancing agricultural inundation modeling accuracy and drainage improvement projects.