Flora of Mt. Samyeong (Yanggu-gun, Gangwon-do) in DMZ Area of Korea

Shin, Hyun Tak; An, Jong Bin; Yoon, Jung Won; Lee, Jun Woo; Jung, Su Young; Heo, Tae Im

doi:10.7732/kjpr.2017.30.2.191

Cited by 4 publications

(1 citation statement)

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“…These limitations have also been confirmed in related studies on the CACZ boreal forest ecosystem. In order to propose mid-to long-term management plans for the protection of native plants from invasive species in the ecosystem, a number of studies have been conducted through vegetation surveys in areas with less human disturbance to identify rare plants, endemic plants, naturalized plants [1][2][3][4][5][6][7], and northern lineage plants [8]. The categorization and grading of wetlands formed by topographical characteristics have also been studied by evaluating factors such as vegetation, hydrology, hydraulics, human landscapes, and disturbances [9,10].…”

Section: Introductionmentioning

confidence: 99%

Empirical Comparison of Supervised Learning Methods for Assessing the Stability of Slopes Adjacent to Military Operation Roads

Kwon

Pan

Kim

et al. 2023

Forests

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The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and slope failures. In this study, a machine-learning-based method was used to assess slope stability by introducing the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) approaches. These classification models were trained and evaluated on 393 slope stability cases from 2009 to 2019 to assess slope stability in the northern area of the Civilian Control Line, South Korea. For comparison, the performance of these classification models was measured by considering the accuracy, Cohen’s kappa, F1-score, recall rate, precision, and area under the ROC curve (AUC). Furthermore, 14 influencing factors (slope, vegetation, structure conditions, etc.) were considered to explore feature importance. The evaluation and comparison of the results showed that the performance of all classifier models was satisfactory for assessing the stability of the slope, the ability of LR was validated (accuracy = 0.847; AUC = 0.838), and XGBoost proved to be the most efficient method for predicting slope stability (accuracy = 0.903; AUC = 0.900). Among the 14 influencing factors, the external condition was the most important. The proposed supervised learning method offers a promising method for assessing slope status, may be beneficial for government agencies in early-stage risk mitigation, and provides a database for efficient restoration management.

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