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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.1
- 수록면
- 113 - 134 (22page)
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초록· 키워드
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Mountain areas in the southern western corner ofthe Kingdom of Saudi Arabia frequently suffer from various typesof landslides due to rain storms and anthropogenic activities. Toresolve the problem related to landslides, landslide susceptibilitymap is important as a quick and safe mitigation measure and tohelp making strategic planning by identifying the most vulnerableareas. This paper summarizes findings of landslide susceptibilityanalysis at Al-Hasher area, Jizan, KSA, using two statistical models:frequency ratio and index of entropy models with the aid ofGIS tools and remote sensing data. The landslide locations (inventorymap) were identified in the study area using historical records,interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic mapsof 1:10,000 scale, and multiple field investigations. A total of 207landslides (80% out of 257 detected landslides) were randomlyselected for model training, and the remaining 50 landslides (19%)were used for the model validation. Ten landslide conditioning factorsincluding slope angle, slope-aspect, altitude, curvature, lithology,distance to lineaments, normalized difference vegetation index (NDVI),distance to roads, precipitation, and distance to streams, were extractedfrom spatial database. Using these conditioning factors and landslidelocations, landslide susceptibility and weights of each factorwere analyzed by using frequency ratio and index of entropy models.
Our findings showed that the existing landslides of high and very highsusceptibility classes cover nearly 80.4% and 79.1% of the susceptibilitymaps produced by frequency ratio and index of entropy modelsrespectively. For verification, receiver operating characteristic (ROC)curves were drawn and the areas under the curve (AUC) were calculatedfor success and prediction rates. For success rate the resultsrevealed that for the index of entropy model (AUC = 77.9%) is slightlylower than frequency ratio model (AUC = 78.8%). For the predictionrate, it was found that the index of entropy model (AUC = 74.9%)is slightly lower than the frequency ratio model (AUC = 76.7%).
The landslide susceptibility maps produced from this study couldhelp decision makers, planners, engineers, and urban areas developersto make suitable decisions.
목차
등록된 정보가 없습니다.
참고문헌 (87)
참고문헌 신청
Akgun, A. / 2007 / GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region / Environmental Geology 51:1377~1387
Akgun, A. / 2012 / An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm / Computers & Geosciences 38:23~34
Aleotti, P. / 1999 / Landslide hazard assessment: summary review and new perspectives / Bulletin of Engineering Geology and the Environment 58:21~44
Atkinson, P. / 1998 / Generalised linear modelling of susceptibility for landsliding in the Central Apennines, Italy / Computers & Geosciences 24:373~385
Ayalew, L. / 2005 / The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, Central Japan / Geomorphology 65:15~31
Akgun, A. / 2012 / An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm / Computers & Geosciences 38:23~34
Aleotti, P. / 1999 / Landslide hazard assessment: summary review and new perspectives / Bulletin of Engineering Geology and the Environment 58:21~44
Atkinson, P. / 1998 / Generalised linear modelling of susceptibility for landsliding in the Central Apennines, Italy / Computers & Geosciences 24:373~385
Ayalew, L. / 2005 / The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, Central Japan / Geomorphology 65:15~31
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