• Journal of Korea Water Resources Association
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• v.37 no.8
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• pp.643-651
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• 2004

In this paper, the Hydrologic Simulation Program-Fortran (HSPF) was validated to estimate the pollutant loads from rural small watershed. The study watershed was the HP#6 subwatershed in Balhan reservoir watershed, located southwest from Suwon. The drainage area of HP#6 study watershed was 3.85$\textrm{km}^2$. Parameters of the HSPF model related to hydrology and water quality were calibrated from 1996 to 1997, and validated from 1999 to 2000 using observed hydrologic and water quality data. The average simulated runoff ratio for the calibration period was 0.579 and the measured runoff ratio was 0.583. The root mean square error (RMSE) for runoff during the calibration period was 2.1mm and correlation coefficient ($R^2$) was 0.92. Regarding the total nitrogen simulation, the RMSE was 0.086kg/ha/day and $R^2$ was 0.81 for the calibration period. In the case of total phosphorus, the RMSE was 0.012kg/ha/day and $R^2$ was 0.70 for the calibration period.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.493-499
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• 2014

The Pythagorean won-loss formula postulated by James (1980) indicates the percentage of games as a function of runs scored and runs allowed. Several hundred articles have explored variations which improve RMSE by original formula and their fit to empirical data. This paper considers a variation on the formula which allows for variation of the Pythagorean exponent. We provide the most suitable optimal exponent in the Pythagorean method. We compare it with other methods, such as the Pythagenport by Davenport and Woolner, and the Pythagenpat by Smyth and Patriot. Finally, our results suggest that proposed method is superior to other tractable alternatives under criterion of RMSE.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Steel and Composite Structures
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• v.49 no.2
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• pp.231-244
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• 2023

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R² and RMSE) and the most accurate model BBO (R²=0.9481, RMSE=4.71878 for training and R²=0.94355, RMSE=5.1338 for testing) and TLBO (R²=0.948, RMSE=4.70822 for training and R²=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• Proceedings of the KAIS Fall Conference
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• 2011.12a
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• pp.105-108
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• 2011

본 연구에서는 기준국을 달리함으로써 관측 횟수를 달리하여 RTK-GPS 측량을 시행하여 보고 이에대한 정확도 분석을 토대로 지적측량에 효율적인 적용 방안을 제시하여 보고자 하였다. 실험지역을 선정하고 기준국을 달리하여 관측 후 기존 TS 성과와 비교한 결과, 제1기준국은 X좌표의 RMSE가 ${\pm}0.024m$, Y좌표의 RMSE가 ${\pm}0.016m$로 산출되었고, 제2기준국은 X좌표의 RMSE가 ${\pm}0.040m$, Y좌표의 RMSE가 ${\pm}0.029m$로 산출되었다. 이는 모두 현행 지적법령에서 규정하고 있는 성과인정 범위 이내의 오차이고, 더불어 두 성과의 차이는 크지 않았다. 따라서 GPS 위성자료 수신에 장애가 없다면 1회 관측으로도 충분히 안정적인 성과의 취득이 가능한 것으로 나타났다. 다만, 측량 환경에 따라 주변에 수신에 제약을 받는 요소가 있다면 이러한 지역에 대해서는 성과의 안정적인 취득을 위해 2회 이상의 관측이 필요할 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.155-163
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• 2010

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on-board the NASA's Terra spacecraft provides along-track digital stereo image data at 15m resolution with a base-height ratio 0.6. Automated stereocorrelation procedure was implemented using the ENVI 4.1 software to derive DEMs with $15m{\times}15m$ in 43km long and 50km wide area using the ASTER stereo images. The accuracy of DEMs was analyzed in comparison with those which were obtained from digital topographic maps of 1:25,000 scale. Results indicate that RMSE in elevation between ${\pm}7$ and ${\pm}20m$ could be achieved. Excluding cloud, water and building areas as the factors which make RMSE value exceeding 10m, the accuracy of DEMs showed RMSE of ${\pm}5.789m$. Therefore for the purpose of elevating accuracy of topographic information, we intended to detect the cloud areas and shadow areas by a landcover classification method, remove those areas on the ASTER DEM and then replace with those areas detached from the cartographic DEM by band math.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.485-490
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• 2006

In this study, the Digital Surface Models of various spatial resolutions were constructed using LIDAR point data on Digital Photogrammetric System. Then, the accuracies of each DSM's were evaluated using GPS surveying data. And also, observable features were classified and their accuracies were evaluated to verify the availability for Ground Control Point. On Socet Set, Digial Photogrametric System 5 DSM's of which spatial resolutions were 0.15m, 0.5m, 1.0m, 2.5m and 5.0m were constructed and the accuracies of eahc DSM's evaluated in RMSE. The RMSE's of each DSM's were 0.03m, 0.05m, 0.08m, 0.12m and 0,19m. The building feature was observable in DSM's of which spatial resolutions were 0.15m, 0.30m and 0.50m. On the contrary, it could hardly be observed in those of other spatial resolutions. In comparison with the digital map at the scale of 1:1,000, the DSM at the spatial resolution of 0.lim was shifted horizaltally by 0.6m-0.7m of RMSE in each X, Y direction. Therefore, GCP of which horizontal RMSE is better than 1m can be obtained from the DSM at the spatial resolution of 0.15m, of which vertical RMSE is 0.03m-0.19m as the RMSE of DSM. This point cannot be used in aerial triangulation of cartography but can be used for GCP in modeling of satellite image at the moderate resolution.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.