• Title/Summary/Keyword: Talus Potential Index

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Extraction of the Talus Distribution Potential Area Using the Spatial Statistical Techniques - Focusing on the Weight of Evidence Model - (공간통계기법을 이용한 애추 분포 가능지역 추출 - Weight of evidence 기법을 중심으로 -)

  • Yu, Jaejin;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.21 no.4
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    • pp.133-147
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    • 2014
  • Reducing the range of target landform, is required to save the time and cost before real field survey in the case of inaccessible landform such as talus. In this study, Weight of Evidence modeling, which is a Target-driven spatial analysis statistics methods, has been applied to reduce the field survey range of target landform. In order to apply the Weight of Evidence analysis, a likelihood ratio was calculated on the basis of the result of correlation analysis between geomorphic factors and GIS information after selection of geomorphic factors regarding talus. A best combination, which has the biggest possibility for Talus Potential Index, was found by using SRC and AUC methods after calculating the number of cases for each thematic maps. This combination which includes aspect, geology, slope, land-cover, soil depth and soil drainage factors, showed quite high accuracy by 74.47% indicating the ratio of real existent talus to potential talus distribution.

Prediction and Verification of Distribution Potential of the Debris Landforms in the Southwest Region of the Korean Peninsula (한반도 서남부 암설사면지형의 분포가능성 예측 및 검증)

  • Lee, Seong-Ho;Jang, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.27 no.2
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    • pp.1-17
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    • 2020
  • This study evaluated a debris landform distribution potential area map in the southwest region of the Korean peninsula. A GIS spatial integration technique and logistic regression method were used to produce a distribution potential area map. Seven topographic and environmental factors were considered for analysis and 28 different data set were combined and used to get most effective results. Moreover, in an accuracy assessment, the extracted results of the Distribution Potential area were evaluated by conducting a cross-validation module. Block stream showed the highest accuracy in the combination No. 6, and that DEM (digital elevation model) and TWI (topographic wetness index) have relatively high influences on the production of the Block stream Distribution Potential area map. Talus showed the highest accuracy in the combination No. 13. We also found that slope, TWI and geology have relatively high influences on the production of the Talus Distribution Potential area map. In addition, fieldwork confirmed the accuracy of the input data that were used in this study, and the slope and geology were also similar. It was also determined that these input data were relatively accurate. In the case of angularity, the block stream was composed of sub-rounded and sub-angular systems and Talus showed differences according to the terrain formation. Although the results of the rebound strain measurement using a Schmidt's hammer did not shown any difference in topographic conditions, it is determined that the rebound strain results reflected the underlying geological setting.

Extraction of Potential Area for Block Stream and Talus Using Spatial Integration Model (공간통합 모델을 적용한 암괴류 및 애추 지형 분포가능지 추출)

  • Lee, Seong-Ho;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.26 no.2
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    • pp.1-14
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    • 2019
  • This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.