Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan

Ayalew, Lulseged; Yamagishi, Hisakazu; Ugawa, Norimitsu

doi:10.1007/s10346-003-0006-9

Cited by 526 publications

(260 citation statements)

References 12 publications

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“…On the whole, these methods can be summarized into two categories: qualitative and quantitative methods. Qualitative methods mainly refer to site analysis and overlay analysis of thematic maps (for example, geomorphological and geological characteristics) based on expert experience (Anbalagan and Singh 1996;Ayalew et al 2004). Quantitative methods mainly refer to various statistical analyses (Carrara et al 1991;Tang et al 2011a;Yoshimatsu and Abe 2006;Kamp et al 2008), such as artificial neural network (ANN) (Lee and Evangelista 2006;Yilmaz 2010), support vector machine (SVM) (Yao et al 2008;Yilmaz 2010;Xu et al 2012), Bayesian Network (Song et al 2012) and Logistic Regression (Dong et al 2011;Nefeslioglu et al 2006;Xu et al 2013a, b).…”

Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent postearthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50°and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of 123Nat Hazards (2015) 75:2467-2487 DOI 10.1007/s11069-014-1441 landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

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Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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“…In the literature, there are several validated studies that indicate that the most critical vulnerability class, normally, occupies between 3.5% and 8.0% of the analyzed basins (Kayastha et al 2013;Bai et al 2009;Ayalew et al 2004;and Akinci et al 2011). …”

Section: -Resultsmentioning

confidence: 99%

Susceptibility and Vulnerability to Landslides—Case Study: Basin of River Bengalas—City of Nova Friburgo—Brazil

Silva

Sampaio

Corte‐Real

et al. 2014

Engineering Geology for Society and Territory - Volume 1

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Landslides have frequently occurred in last years, due to the disorderly growth of the cities and the occupation of risk areas by the poor population, causing social, environmental and economic impacts. Urban areas in expansion move to geologically unstable areas and topographically inclined, such as the River Bengalas Basin, located in the city of Nova Friburgo, mountainous region of the State of Rio de Janeiro, Brazil. This article aims to present the model developed and used to evaluate the susceptibility and vulnerability of the River Bengalas Basin to landslides, which in January 2011, with the occurrence of heavy rains, caused landslides that impacted in the death of 429 people in city of Nova Friburgo. For the case study, several investigations have been made related to the areas of the basin, such as slope, soil conditions, lithology, land use and cover, vertical curvature, horizontal curvature, and precipitation data. With this study it was possible to understand how the natural and anthropics elements of the basin are related to the local dynamics of the disasters regarding to their interferences in the induction of landslides; evaluate the effectiveness of the guidelines of the Plano Diretor Participativo do Município de Nova Friburgo regarding the landslides; identify the susceptible and vulnerable basin areas to landslides and calculate the rates of susceptibility and vulnerability to landslides from new calculation model proposed..

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“…Five (5) classes (Table 8) of soil susceptibility (very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility) were generated and used for mapping. This linear combination approach is one of the best known and most commonly used MCDA methods (Malczewski, 1999;Ayalew et al, 2004;Tsangaratos and Rozos, 2013) for susceptibility analysis. The summing ranking-based scores at cell unit provide the level of exposure of the unit area taken into consideration all parameters leading to erosion at this micro-unit while maintaining the variation in the spatial interplay of the parameters at the macro-level.…”

Section: Factor Of Soil Erosion Susceptibility Integrationmentioning

confidence: 99%

Land susceptibility to soil erosion in Orashi Catchment, Nnewi South, Anambra State, Nigeria

et al. 2018

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Abstract. Soil erosion is one of the most critical environmental hazards that causes land degradation and water quality challenges. Specifically, this phenomenon has been linked, among other problems, to river sedimentation, groundwater pollution and flooding. This paper assesses the susceptibility of Orashi River Basin (ORB) to soil erosion for the purpose of erosion control measures. Located in the South Eastern part of Nigeria, the ORB which covers approximately 413.61 km 2 is currently experiencing one of the fastest population growth rate in the region. Analysis of the soil erosion susceptibility of the basin was based on four factors including; rainfall, Land use/Land cover change (LULC), slope and soil erodibility factor (k). The rainfall was assumed to be a constant and independent variable, slope and soil types were categorised into ten (10) classes each while the landuse was categorised into five classes. Weight was assigned to the classes based on the degree of susceptibility to erosion. An overlay of the four variables in a GIS environment was used to produce the basin susceptibility to soil erosion. This was based on the weight index of each factors. The LULC analysis revealed that built-up land use increased from 26.49 km 2 (6.4 %) in year 1980 to 79.24 km 2 (19.16 %) in 2015 at an average growth rate of 1.51 km 2 per annum while the light forest decreased from 336.41 km 2 (81.33 %) in 1980 to 280.82 km 2 (67.89 %) in 2015 at an average rate 1.59 km 2 per annum. The light forest was adjudged to have the highest land cover soil erosion susceptibility. The steepest slope ranges between 70 and 82 • (14.34 % of the total land area) and was adjudged to have the highest soil susceptibility to erosion. The total area covered of the loamy soil is 112.37 km 2 (27.07 %) with erodibility of 0.7. In all, the overlay of all the variables revealed that 106.66 km 2 (25.70 %) and 164.80 km 2 (39.7 %) of the basin has a high and very high susceptibility to soil erosion. The over 50 % high susceptibility of catchment has serious negative implications on the surface water in terms of water quality and downstream siltation with great consequences on biodiversity and ecosystem services including domestic and industrial usage.

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Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan

Cited by 526 publications

References 12 publications

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Susceptibility and Vulnerability to Landslides—Case Study: Basin of River Bengalas—City of Nova Friburgo—Brazil

Land susceptibility to soil erosion in Orashi Catchment, Nnewi South, Anambra State, Nigeria

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