• Title/Summary/Keyword: RMSE

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Evaluation of Airborne LiDAR Data using Field Surveyed Ground Control Points (현지 측량기준점을 이용한 LiDAR 데이터의 정확도 검증)

  • Wie, Gwang-Jae;Yang, In-Tae;Suh, Young-Woon;Sim, Jung-Min
    • Journal of Korean Society for Geospatial Information Science
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    • v.14 no.4 s.38
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    • pp.11-18
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    • 2006
  • In this paper, airborne LiDAR data were evaluated in horizontal and vertical accuracy. By using zigzag scanning type of LiDAR, GCPs are not tested directly. So points around GCPs were used in this evaluation. Building corner points were made from LiDAR's building planar and compared with ground surveyed GCPs, in horizontal accuracy test. Its accuracy shows 19cm average and 21cm RMSE and 15 points were within 20cm among 16 points. In vertical accuracy test, 41 GCPs were used and it shows 11cm average and 14cm RMSE and 75% of GCPs were within 15cm. This could be a criterion in topographic map modification and basic geographic DB and 3D data construction using airborne LiDAR data.

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City Gas Pipeline Pressure Prediction Model (도시가스 배관압력 예측모델)

  • Chung, Won Hee;Park, Giljoo;Gu, Yeong Hyeon;Kim, Sunghyun;Yoo, Seong Joon;Jo, Young-do
    • The Journal of Society for e-Business Studies
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    • v.23 no.2
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    • pp.33-47
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    • 2018
  • City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

Generation of Digital Orthoimage using ADS40 Images (ADS40영상에 의한 수치정사영상 생성)

  • Lee, Jun-Hyuk;Lee, Young-Jin
    • Spatial Information Research
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    • v.16 no.3
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    • pp.317-330
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    • 2008
  • In this paper, the acquisition of digital imagery and the orthoimage generation were performed to set up working process. And another purpose of this thesis is to evaluate the accuracy of orthoimage by overlapping digital topographic map and digital cadastral map on it. The digital topographic map and digital cadastral map were superimposed on the orthoimage to check the accuracy as another approach of evaluation. The RMSE is ${\pm}0.364m$ in X direction and ${\pm}0.413m$ in Y direction with digital topographical maps(1/5,000). And the RMSE is ${\pm}1.283m$ in X direction and ${\pm}1.085m$ in Y direction with digital cadastral map. It is necessary for the application of a newly developed digital aerial camera to make an exact synchronization between GPS/IMU data and image data, use a technology for setting a standard image resolution and the number of ground control points.

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Development and Evaluation of an Ensemble Forecasting System for the Regional Ocean Wave of Korea (앙상블 지역 파랑예측시스템 구축 및 검증)

  • Park, JongSook;Kang, KiRyong;Kang, Hyun-Suk
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.30 no.2
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    • pp.84-94
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    • 2018
  • In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong River (focusing on water quality and quantity factors) (머신러닝 기법을 활용한 낙동강 중류 지역의 Chl-a 예측 알고리즘 비교 연구(수질인자 및 수량 중심으로))

  • Lee, Sang-Min;Park, Kyeong-Deok;Kim, Il-Kyu
    • Journal of Korean Society of Water and Wastewater
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    • v.34 no.4
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    • pp.277-288
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    • 2020
  • In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

Performance and Root Mean Squared Error of Kernel Relaxation by the Dynamic Change of the Moment (모멘트의 동적 변환에 의한 Kernel Relaxation의 성능과 RMSE)

  • 김은미;이배호
    • Journal of Korea Multimedia Society
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    • v.6 no.5
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    • pp.788-796
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    • 2003
  • This paper proposes using dynamic momentum for squential learning method. Using The dynamic momentum improves convergence speed and performance by the variable momentum, also can identify it in the RMSE(root mean squared error). The proposed method is reflected using variable momentum according to current state. While static momentum is equally influenced on the whole, dynamic momentum algorithm can control the convergence rate and performance. According to the variable change of momentum by training. Unlike former classification and regression problems, this paper confirms both performance and regression rate of the dynamic momentum. Using RMSE(root mean square error ), which is one of the regression methods. The proposed dynamic momentum has been applied to the kernel adatron and kernel relaxation as the new sequential learning method of support vector machine presented recently. In order to show the efficiency of the proposed algorithm, SONAR data, the neural network classifier standard evaluation data, are used. The simulation result using the dynamic momentum has a better convergence rate, performance and RMSE than those using the static moment, respectively.

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Analysis of Integrated GPS/GLONASS/BDS Positioning Accuracy using Low Cost Receiver (저가형 수신기를 이용한 GPS/GLONASS/BDS 통합 측위 정확도 분석)

  • Tae, Hyun U;Park, Kwan Dong;Kim, Mi So
    • Journal of Korean Society for Geospatial Information Science
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    • v.23 no.4
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    • pp.49-55
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    • 2015
  • This paper explains major considerations for integrated GPS/GLONASS/BDS positioning, and then analyzes integrated GNSS positioning accuracies based on low-cost receivers in open-sky and poor reception environments. In an open-sky environment, horizontal RMSE of the integrated system positioning is about 1.2m. It shows improved result compared with single system positioning, the improvement ratio was 17-55%. In poor reception environments, we sometimes could not do positioning because the number of visible satellites gets below four. In an integrated positioning mode, the number of visible satellites was always higher than four, allowing us to find positions all the time. The horizontal RMSE of the integrated system positioning in poor reception environments is about 6.4m. Compared with single system positioning;the integrated system positioning shows better performance and the improvement ratio was 8-47% for the horizontal directions.

Influence of Fertilizing Methane Fermentation Digested Sludge to Rice Paddy on Growth of Rice and Rice Taste (메탄발효 소화액 시용이 벼 생육과 식미에 미치는 영향)

  • Ryu, Chan-Seok;Lee, Choung-Keun;Umeda, Mikio;Lee, Seung-Kyu
    • Journal of Biosystems Engineering
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    • v.34 no.4
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    • pp.269-277
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    • 2009
  • In this research, the vegetation growth and rice taste of the liquid fertilizer applied fields (LF) were compared with those of chemical fertilizer applied fields(CF) in order to confirm the possibility of methane fermentation digested sludge as liquid fertilizer using precision agriculture and remote sensing technology. In panicle initiation stage, the vegetation growth at LF was 60%~80% of it at CF and there were significant difference of nitrogen contents between CF and LF. The estimation model of nitrogen contents was established by GNDVI (R=0.607, RMSE=$1.04\;g/m^2$, n=36, p<0.01). In heading stage, vegetation growth at LF went close to it at CF as ratio of 80%~95%. The nitrogen content estimation model was also established (R=0.650, RMSE=$1.73\;g/m^2$, n=35, p<0.01) and there were significant difference of spatial variability between LF and CF. There were not significant difference of rice taste and it's elements, when three samples, which were more than twice of standard deviation, were excepted. The protein contents estimation model using GNDVI of before harvesting (R=0.700, RMSE=0.470%, n=29, p<0.01) were more suitable to predict the protein contents at harvesting comparing with it of heading stage(R=0.610, RMSE=0.521%, n=29, p<0.01).

Examination of KGD2002 Results of the National Geodetic Network Adjustment (국가기준점망의 KGD2002성과산출과 현지검측에 의한 분석)

  • Lee, Young-Jin;Choi, Yun-Soo;Koh, Hyoung-Kon;Hwang, Byoung-Chul
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.25 no.5
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    • pp.465-474
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    • 2007
  • This paper focuses on examining and evaluating results of the Korean Geodetic Datum 2002 (KGD2002) of the national horizontal network adjustment. To this end, 137 geodetic control points were independently observed by GPS technology. After processing all the observations, their results were compared with ones derived by the national network adjustment which was recently performed to determine new KGD 2002 coordinate sets over the national geodetic control points. The comparisons results showed that RMSE was ${\pm}2.7cm$ and ${pm}6.5cm$ in horizontal and vertical component in the case of GPS network, whereas RMSE was ${\pm}3.0cm$, in horizontal component in the case of EDM network.

The Comparison of Estimation Methods for the Missing Rainfall Data with spatio-temporal Variability (시공간적 변동성을 고려한 강우의 결측치 추정 방법의 비교)

  • Kim, Byung-Sik;Noh, Hui-Seong;Kim, Hung-Soo
    • Journal of Wetlands Research
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    • v.13 no.2
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    • pp.189-197
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    • 2011
  • This paper reviewed application of data-driven method, distance-weighted method(IDWM, IEWM, CCWM, ANN), and radar data method estimated of missing raifall data. To evaluate these methods, statistics was compared using radar and station rainfall data from Imjin-river basin. The range of RMSE values calculated for CCWM, ANN was 1.4 to 1.79mm, and the range of RMSE values estimated data used for radar rainfall data was 0.05 to 2.26mm. Spatial characteristics is considered to Radar rainfall data rather than station rainfall data. Result suggest that estimated data used for radar data can impove estimation of missing raifall data.