• Geomechanics and Engineering
• /
• v.38 no.4
• /
• pp.439-453
• /
• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• Atmosphere
• /
• v.24 no.3
• /
• pp.433-444
• /
• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.1
• /
• pp.99-118
• /
• 2012

The aim of this study is to define a correlation between spatial distribution characteristics of FFG(Functional Feeding Groups) of aquatic insects and related environmental factors in the Geum River based on the theory of RCC(River Continuum Concept). For that objective we had used SMRA(Stepwise Multiple Regression Analysis) method to analyze close relationship between the distribution of aquatic insects and the physical and chemical factors that may affect their inhabiting environment in the study area. And then, a probabilistic method named Frequency Ratio Model(FRM) and spatial analysis function of GIS were applied to produce a predictive distribution map of biota community considering their distribution characteristics according to the environmental factors as related variables. As a result of SMRA, the values of decision coefficient for factors of elevation, stream width, flow velocity, conductivity, temperature and percentage of sand showed higher than 0.5. Therefore these 6 environmental factors were considered as major factors that might affect the distribution characteristics of aquatic insects. Finally, we had calculated RMSE(Root Mean Square Error) between the predicted distribution map and prior survey database from other researches to verify the result of this study. The values of RMSE were calculated from 0.1892 to 0.4242 according to each FFG so we could find out a high reliability of this study. The results of this study might be used to develop a new estimation method for aquatic ecosystem with macro invertebrate community and also be used as preliminary data for conservation and restoration of stream habitats.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.4
• /
• pp.61-71
• /
• 2015

Quantitative variability of AMSR2 (Advanced Microwave Scanning Radiometer 2) soil moisture data shows that the remotely sensed soil moisture is underestimated during Spring and Winter seasons and is overestimated during Summer and Fall seasons. Therefore the bias correction of the remotely sensed data is essential for the purpose of water resource management. To enhance their applicability, the bias of AMSR2 soil moisture data was corrected using ground observation data at Cheorwon Chuncheon, Suwon, Cheongju, Jeonju, and Jinju sites. Test statistics demonstrated that the correlation coefficient R is improved from 0.107~0.328 to 0.286~0.559 and RMSE is improved from 9.46~14.36 % to 5.38~9.62 %. Bias correction using ground network data improved the applicability of remotely sensed soil moisture data.

• The Pure and Applied Mathematics
• /
• v.20 no.1
• /
• pp.59-70
• /
• 2013

The purpose of this study was to develop a zero-inflated Rasch (ZI-Rasch) model, a combination of the Rasch model and the ZIP model. The ZI-Rasch model was considered in this study as an appropriate alternative to the Rasch model for zero-inflated data. To investigate the relative appropriateness of the ZI-Rasch model, several analyses were conducted using PROC NLMIXED procedures in SAS under various simulation conditions. Sets of criteria for model evaluations (-2LL, AIC, AICC, and BIC) and parameter estimations (RMSE, and $r$) from the ZI-Rasch model were compared with those from the Rasch model. In the data-model fit indices, regardless of the simulation conditions, the ZI-Rasch model produced better fit statistics than did the Rasch model, even when the response data were generated from the Rasch model. In terms of item parameter ${\lambda}$ estimations, the ZI-Rasch model produced estimates similar to those of the Rasch model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.2158-2162
• /
• 2009

본 연구에서는 실측자료와 염분수지 계산식을 이용하여 이원 담수호의 염도변화를 모의하였다. 이원담수호는 충남 태안군 원북면 방갈리 민어도에서 이원면 관리 반금봉을 연결하며, 간척농지 개발사업은 1990년에 착공하여 1997년 최종물막이 공사가 완료되었다. 담수호의 염도변화를 모의하기 위한 유입량과 유출량자료는 배수갑문 운용자료와 일별 수위자료로부터 산정하였다. 유출량은 내 외수위 조건에 따라 계산식을 적용하였으며, 이와 함께 배수갑문 역유입량, 방조제 누수량, 호저토 확산 염분량을 계산하여 염분수지식에 적용하여, 2006년부터 2008년까지의 염도변화를 모의하였다. 모의치를 실측치와 비교한 결과, 결정계수($R^2$)가 0.95${\sim}$0.98의 값을 보였다. 이와 함께 RMSE를 통해 그 적용성을 검토하여 3년간의 이원 담수호의 염도변화를 모의하였다.

• Journal of Biosystems Engineering
• /
• v.33 no.4
• /
• pp.269-275
• /
• 2008

This study was conducted to design an impedance sensor that can measure soil water content of soils. Partial least square regression (PLSR) was applied to soil impedance data preprocessed with a smoothing method. An optimal sub-spectrum size and wavelength range were determined by comparing the coefficient of determination ($R^2$) and root mean square error (RMSE) of the PLSR models obtained using soil impedance data. various PLS analysis. Based on the PLSR analysis, it would be concluded that the optimal spectrum measurement range was $32.0{\sim}50.0\;MHz$ with the optimal sub-spectrum size of about 18.5 MHz.

• Journal of Environmental Science International
• /
• v.17 no.9
• /
• pp.1039-1052
• /
• 2008

The SWAT model developed by the USDA-Agricultural Research service for the prediction of rainfall run-off, sediment, and chemical yields in a basin was applied to Jeju Island watershed to estimate the amount of runoff. The research outcomes revealed that the estimated amount of runoff for the long term on 2 water-sheds showed fairly good performance by the long-term daily runoff simulation. The watershed of Chunmi river located the eastern region in Jeju Island, after calibrations of direct runoff data of 2 surveys, showed the similar values to the existing watershed average runoff rate as 22% of average direct runoff rate for the applied period. The watershed of Oaedo river located the northern region showed $R^2$ of 0.93, RMSE of 14.92 and ME of 0.70 as the result of calibrations by runoff data in the occurrence of 7 rainfalls.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.1147-1151
• /
• 2004

본 연구에서는 소유역에서의 오염총량을 추정하기 위한 오염총량 추정모형인 SWAT 모형과 HSPF모형의 유출특성을 비교분석하기 위해 발안유역의 $HP\#6$ 소유역을 시험유역으로 선정하고 유역 수문모니터링을 수행하였으며, 시험유역의 도형자료를 구축하여 SWAT 모형과 HSPF 모형을 적용하였다. 유출량에 대하여 모형의 보정과 검정결과 유출량은 HSPF 모형의 모의유출량이 SWAT 모형보다 실측치에 더 유사한 값을 보였다. 통계적인 변량을 이용하여 실측치와 SWAT 모형과 HSPF 모형의 모의치를 비교하여 평가한 결과 RMSE는 각각 5.19mm/day, 6.03mm/day, RMAE는 0.48mm/day, 0.49mm/day, 결정계수$(R^2)$는 0.86, 0.84로 모의 되었다.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.49 no.4
• /
• pp.73-79
• /
• 2007

In the evaluation of rural amenity, it is hard to estimate the worth of rural amenity as numerical values. To evaluate the worth of an abstract idea, information measure technique(IMT) can be used. In this research, rural amenity priority assessment model(RAPAM) was developed adapting IMT. To apply the developed model for assessment of rural amenity, an encyclopedia was used as information gathering and utilizing system(IGUS). The application result of the developed model which was compared with the result of survey have similar trend. $R^{2}$ is 0.8539, RMSE is 0.382 and the applicability of RAPAM was proved.