지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.
목차
등록된 정보가 없습니다.
참고문헌 (44)
참고문헌 신청
Akgun, A / 2012 / A comparison of landslide susceptibility maps produced by logistic regression, multicriteria decision and likelihood ratio methods:a case study at Izmir / Turkey, Landslides 9(1):93~106
Akgun, A / 2008 / Landslide susceptibility mapping for a landslide-prone area by likelihood-frequency ratio and weighted linear combination models / Envi Geol 54(6):1127~1143
Ayalew, L / 2005 / Landslides in Sado Island of Japan: GISbased susceptibility mapping with comparisons of results from two methods and verifications / Eng Geol 81(4):432~445
Bednarik, M / 2010 / Landslide susceptibility assessment of the Kralovany Liptovsky Mikulas railway case study / Phys Chem 35(5):162~171
Bell, R / 2012 / Assessment of landslide age, persistence and human impact using airborne laser scanning / Geogr Annaly 94:135~156
Akgun, A / 2008 / Landslide susceptibility mapping for a landslide-prone area by likelihood-frequency ratio and weighted linear combination models / Envi Geol 54(6):1127~1143
Ayalew, L / 2005 / Landslides in Sado Island of Japan: GISbased susceptibility mapping with comparisons of results from two methods and verifications / Eng Geol 81(4):432~445
Bednarik, M / 2010 / Landslide susceptibility assessment of the Kralovany Liptovsky Mikulas railway case study / Phys Chem 35(5):162~171
Bell, R / 2012 / Assessment of landslide age, persistence and human impact using airborne laser scanning / Geogr Annaly 94:135~156
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