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    닫기

    머신러닝 크리깅을 이용한 산림재적 추정 = Estimating forest timber volume using machine learning kriging

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    https://www.riss.kr/link?id=T15918029

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    부가정보

    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    영어
    한국어
    In this paper, we devise machine learning kriging with the application of traditional regression kriging, a statistical geographical technique, and utilize it for estimating forest timber
    volume to confirm its performance. The site of the study was Bonghwa-gun, Gyeongsangbukdo, and aviation LiDAR (Light Detection And Ranging) data and field survey data were used
    for estimating forest timber volume. The performance comparison of the model shows that the
    SVMRK (Support Vector Machine Residual Kriging) model, a combination of Support Vector
    Machine (SVM) and Residual Kriging, shows the best performance on the RMSE metrics. In
    MAPE metrics, the LMRK (Linear Model Residual Kriging) model showed the best performance. In addition, in this work, we further consider the model generated by ensemble the
    SVMRK model and the LMRK model 1:1 to conduct performance comparisons. As a result,
    the ensemble model has been identified as the best performing model, which is believed to be
    the result of a mix of advantages and disadvantages of the two models (SVMRK, LMRK).
    In the future, research on the general utilization of the corresponding forest timber volume
    estimation model should also be conducted through various research sites.
    번역하기

    In this paper, we devise machine learning kriging with the application of traditional regression kriging, a statistical geographical technique, and utilize it for estimating forest timber volume to confirm its performance. The site of the study was Bo...

    In this paper, we devise machine learning kriging with the application of traditional regression kriging, a statistical geographical technique, and utilize it for estimating forest timber
    volume to confirm its performance. The site of the study was Bonghwa-gun, Gyeongsangbukdo, and aviation LiDAR (Light Detection And Ranging) data and field survey data were used
    for estimating forest timber volume. The performance comparison of the model shows that the
    SVMRK (Support Vector Machine Residual Kriging) model, a combination of Support Vector
    Machine (SVM) and Residual Kriging, shows the best performance on the RMSE metrics. In
    MAPE metrics, the LMRK (Linear Model Residual Kriging) model showed the best performance. In addition, in this work, we further consider the model generated by ensemble the
    SVMRK model and the LMRK model 1:1 to conduct performance comparisons. As a result,
    the ensemble model has been identified as the best performing model, which is believed to be
    the result of a mix of advantages and disadvantages of the two models (SVMRK, LMRK).
    In the future, research on the general utilization of the corresponding forest timber volume
    estimation model should also be conducted through various research sites.

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    목차 (Table of Contents)

    • CHAPTER 1 서론 1
    • CHAPTER 2 분석 방법론 3
    • 2.1 랜덤 프로세스 3
    • 2.2 공간자료의 정상성 4
    • 2.3 세미베리오그램 6
    • CHAPTER 1 서론 1
    • CHAPTER 2 분석 방법론 3
    • 2.1 랜덤 프로세스 3
    • 2.2 공간자료의 정상성 4
    • 2.3 세미베리오그램 6
    • 2.4 회귀 크리깅 (Regression-Kriging) 7
    • 2.5 회귀 크리깅을 응용한 추가적 모형 9
    • 2.6 앙상블 모형 10
    • CHAPTER 3 사례 연구 11
    • 3.1 사용 자료 11
    • 3.2 자료 분석 12
    • CHAPTER 4 결론 16
    • REFERENCES 18
    • ABSTRACT (in ENGLISH) 27
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