Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models

Qiqing Wang; Wenping Li; Maolin Xing; Yanli Wu; Yabing Pei; Dongdong Yang; Hanying Bai

추천

검색

자료유형: 학술저널

저자정보: Qiqing Wang (China University of Mining and Technolog) Wenping Li (China University of Mining and Technolog) Maolin Xing (China University of Mining and Technolog) Yanli Wu (China University of Mining and Technolog) Yabing Pei (China University of Mining and Technolog) Dongdong Yang (China University of Mining and Technolog) Hanying Bai (Guilin University of Technology)

저널정보: 한국지질과학협의회 Geosciences Journal Geosciences Journal Vol.20 No.5

발행연도: 2016.1

수록면: 705 - 718 (14page)

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

The aim of this study was to apply and to verify the use of artificial neural network (ANN) and weight of evidence (WoE) models to landslide susceptibility mapping in the Gongliu county, China, using a geographic information system (GIS). For this aim, in this study, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 163 landslides (70% out of 233 detected landslides) were randomly selected for model training, and the remaining 70 landslides (30%) were used for the model validation. Then, a total number of twelve landslide conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to rivers, distance to roads, lithology, rainfall, normalized difference vegetation index (NDVI), and sediment transport index (STI), were used in the analysis. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by ANN and WoE models. Finally the output maps were validated using the area under the curve (AUC) method. The validation results showed that the ANN model with a success rate of 82.51% and predictive accuracy of 77.31% performs better than WoE (success rate, 79.82%; predictive accuracy, 74.59%) model. Overall, both models showed almost similar results. Therefore, the two landslide susceptibility maps obtained were successful and can be useful for preliminary general land use planning and hazard mitigation purpose.

참고문헌 (88)

참고문헌 신청

Agterberg, F. P. / 1993 / Weights of evidence modeling and weighted logistic regression for mineral potential mapping / Computers in geology 25:13~32

Ahmed, B. / 2014 / Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh / Landslides 12:1~19

Akgun, A. / 2012 / A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods : a case study at İzmir, Turkey / Landslides 9:93~106

Akgun, A. / 2008 / Landslide susceptibility mapping for a landslide-prone area(Findikli, NE of Turkey)by likelihood-frequency ratio and weighted linear combination models / Environmental Geology 54:1127~1143

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

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!