Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

Park, Soyoung; Choi, Chuluong; Kim, Byungwoo; Kim, Jin-Soo

doi:10.1007/s12665-012-1842-5

Cited by 389 publications

(197 citation statements)

References 59 publications

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“…The slope angle map was extracted from the DEM through the slope unit, and the average slope angle of each slope unit was thereafter obtained [43]. The slope map was reclassified into nine classes: (1) The distance-to-river has a particular controlling effect on the development of landslides [17]. Meanwhile, the significant influence of river erosion on landslide development is primarily due to the following aspects: (1) The softening effect of water on a rock mass reduces its strength.…”

Section: Influencing Factorsmentioning

confidence: 99%

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Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Wang

et al. 2017

IJGI

106

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Abstract:The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

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Section: Influencing Factorsmentioning

confidence: 99%

“…Many methods use either a subjective evaluation or an objective evaluation, which limited itself the efficacy of the approach. Therefore, it is necessary to combine the subjective and objective evaluation methods [17].…”

Section: Introductionmentioning

confidence: 99%

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Wang

et al. 2017

IJGI

106

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“…Because of its simplicity and fl exibility, AHP has been widely applied in landslide susceptibility studies (e.g. Yalcin and Bulut 2007;Thanh and Smedt 2011;Park et al 2012;Reis et al 2012;Pourghasemi et al 2012;Hasekioğulları and Ercanoglu 2012;Dragićević et al 2015). In this technique, a reciprocal pairwise matrix is constructed by comparing the factors and assigning a relative importance value to its relation, according to a 9-point continuous scale (Figure 6).…”

Section: Determination Of Landslide Conditioning Factors Weightsmentioning

confidence: 99%

Multi-Criteria Analysis Applied to Landslide Susceptibility Mapping

Brito

Weber

Filho

2017

Rev. Bras. Geomorfol.

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Abstract:This paper presents the application of a multi-criteria analysis (MCA) tool for landslide susceptibility assessment in Porto Alegre municipality, southern Brazil. A knowledge driven approach was used, aiming to ensure an optimal use of the available information. The landslide conditioning factors considered were slope, lithology, fl ow accumulation and distance from lineaments. Standardization of these factors was done through fuzzy membership functions, and evaluation of their relative importance for landslide predisposition was supported by the analytic hierarchy process (AHP), based on local expert knowledge. Finally, factors were integrated in a GIS environment using the weighted linear combination (WLC) method. For validation, an inventory, including 107 landslide points recorded between 2007 and 2013 was used. Results indicated that 8.2% (39.40 km²) of the study area are highly and very highly susceptible to landslides. An overall accuracy of 95% was found, with an area under the receiver operating characteristic (ROC) curve of 0.960. Therefore, the resulting map can be regarded as useful for monitoring landslide-prone areas. Based on the fi ndings, it is concluded that the proposed method is eff ective for susceptibility assessment since it yielded meaningful results and does not require extensive input data. Resumo: Informações sobre o ArtigoEste estudo apresenta a aplicação de uma ferramenta de análise multi-critério para mapear a suscetibilidade a escorregamentos no município de Porto Alegre, sul do Brasil. Uma abordagem guiada pelo conhecimento de especialistas foi utilizada, com o intuito de otimizar a utilização das informações disponíveis. Os fatores condicionantes dos escorregamentos considerados foram declividade, litologia, acúmulo defl uxo e distância de lineamentos. A padronização desses fatores foi realizada por meio da aplicação de funções fuzzy e a importância relativa de cada um na predisposição do terreno a escorregamentos foi estabelecida com o apoio da técnica AHP (Analytic Hierarchy Process), com base no conhecimento de especialistas locais. Por fi m, a integração dos fatores em ambiente SIG se deu por meio do método denominado Combinação Linear Ponderada (WLC). Para validar os resultados, utilizou-se um mapa inventário contendo 107 cicatrizes de escorregamentos, registradas entre 2007 e 2013. Os resultados indicam que 8,2% (39,38 km²) da área de estudo possui uma suscetibilidade alta e muito alta a escorregamentos. A validação dos resultados obteve uma exatidão geral de 95%, com uma área abaixo da curva ROC (Receiver Operating Characteristic) de 0,960. Portanto, o mapa obtido pode ser considerado útil para monitorar as áreas propensas a esses processos. Com base nos resultados, conclui-se que o método proposto é efi caz para a avaliação da suscetibilidade, uma vez que os resultados obtidos são robustos e que não foi necessária uma quantidade extensa de dados de entrada.

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“…To do susceptibility mapping, geos-patial techniques (i.e., GIS and remote sensing) play the prime role in integrating all the causative factors with less biasness and error. Different techniques, e.g., qualitative and/or quantitative, have been employed so far in landslide susceptibility mapping [11]. Among which three viz., heuristic, deterministic and statistical are the most common and popular.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach

Islam¹,

Murshed²,

Kabir³

et al. 2017

IJG

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Since creation of spatial data is a costly and time consuming process, researchers, in this domain, in most of the cases rely on open source spatial attributes for their specific purpose. Likewise, the present research aims at mapping landslide susceptibility at the metropolitan area of Chittagong district of Bangladesh utilizing obtainable open source spatial data from various web portals. In this regard, we targeted a study region where rainfall induced landslides reportedly causes causalities as well as property damage each year. In this study, however, we employed multi-criteria evaluation (MCE) technique i.e., heuristic, a knowledge driven approach based on expert opinions from various discipline for landslide susceptibility mapping combining nine causative factors-geomorphology, geology, land use/land cover (LULC), slope, aspect, plan curvature, drainage distance, relative relief and vegetation in geographic information system (GIS) environment. The final susceptibility map was devised into five hazard classes viz., very low, low, moderate, high, and very high, representing 22 km 2 (13%), 90 km 2 (53%); 24 km 2 (15%); 22 km 2 (13%) and 10 km 2 (6%) areas respectively. This particular study might be beneficial to the local authorities and other stake-holders, concerned in disaster risk reduction and mitigation activities. Moreover this study can also be advantageous for risk sensitive land use planning in the study area.

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Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

Cited by 389 publications

References 59 publications

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Multi-Criteria Analysis Applied to Landslide Susceptibility Mapping

Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach

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