Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

Oh, Hyun-Joo; Pradhan, Biswajeet

doi:10.1016/j.cageo.2010.10.012

Cited by 403 publications

(158 citation statements)

References 58 publications

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“…So, all variables in the rock-fall susceptibility map appear effective, and the prediction of the susceptibility map was measured in this way. These results are in agreement with the results of Lee and Sambath (2006), Pradhan (2006, 2007), Oh et al (2009), Jadda et al (2009), Nandi and Shakoor (2009), Oh et al (2009), Pradhan (2010, Pradhan and Lee (2010), Chauchan et al (2010) and Oh and Pradhan (2011) to apply the success rate curve and AUC to investigate the reliability of the landslides susceptibility map. We have also found that 14.…”

Section: Discussionsupporting

confidence: 81%

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

et al. 2012

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This study describes the application of logistic regression to rock-fall susceptibility mapping along 11 km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76 % of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57 %. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall.

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Section: Discussionsupporting

confidence: 81%

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

et al. 2012

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“…Based on the slope shape, the slope can be divided into three types, including concave, convex, and flat (Figure 5c). The influence of curvature on the slope erosion processes is the convergence or divergence of water during downhill flow [61]. (Figure 5b).…”

Section: Influencing Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

Cao

Wang

Chen

et al. 2016

Water

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This study focused on landslide susceptibility analysis mapping of the Xulong hydropower station reservoir, which is located in the upstream of Jinsha River, a rapidly uplifting region of the Tibetan Plateau region. Nine factors were employed as landslide conditioning factors in landslide susceptibility mapping. These factors included the slope angle, slope aspect, curvature, geology, distance-to-fault, distance-to-river, vegetation, bedrock uplift and annual precipitation. The rapid bedrock uplift factor was represented by the slope angle. The eight factors were processed with the information content model. Since this area has a significant vertical distribution law of precipitation, the annual precipitation factor was analyzed separately. The analytic hierarchy process weighting method was used to calculate the weights of nine factors. Thus, this study proposed a component approach to combine the normalized eight-factor results with the normalized annual precipitation distribution results. Subsequently, the results were plotted in geographic information system (GIS) and a landslide susceptibility map was produced. The evaluation accuracy analysis method was used as a validation approach. The landslide susceptibility classes were divided into four classes, including low, moderate, high and very high. The results show that the four susceptibility class ratios are 12.9%, 35.06%, 34.11%and 17.92% of the study area, respectively. The red belt in the high elevation area represents the very high susceptibility zones, which followed the vertical distribution law of precipitation. The prediction accuracy was 85.74%, which meant that the susceptibility map was confirmed to be reliable and reasonable. This susceptibility map may contribute to averting the landslide risk in the future construction of the Xulong hydropower station.

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“…4 shows that 8 contours (No. 7,16,17,21,23,25,28,29) represent the areas of low or very low risk of hazard of sliding. Although the risk of landside is lessened due to the spread of low-incline slopes, the seismicity reaches up to 8 points, while rocks are of high hazard of sliding.…”

Section: Figure 5 Landscape and Environmental Risks And Hazards Causmentioning

confidence: 99%

The Assessment of Landscape and Environmental Risks and Hazards Caused by Landslides in Mountain Areas

Alizade¹

2016

Appl Ecol Env Res

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Abstract. The landslide is a kind of natural disaster responsible for creation of risks and hazards in relation to population and its economic activity. Each year the occurrence of landslides causes killing of people and economic losses, as well as leads to destruction and deterioration of pastures, arable lands, forests and highways. The southeastern slope of Greater Caucasus is one of the most active landslide zones in Azerbaijan. As a result of the occurrence of landslide in the Mughanli area in 2011, 168 houses out of 279 ones became unusable while the village of the same name was moved to a new residential area. Moreover, the 3 km-long part of the Ismayilli-Mughanli-Shamakhi highway near the village became unusable repeatedly. In 2014, USD 52 million was spent in order to eliminate the consequences of landslides. Topographic maps of scale of 1:10000 and also aerospace images were used to assess the landscape-and environmental risks and hazards caused by landslides. Maps of various factors of the same scales, compiled in ArcGIS program were analysed through GIS technologies, which allowed compilation of a map of relevant landscape-and environmental risks and hazards. The carried out studies will enable to estimate natural disasters and reduce the related economic losses.

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Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

Cited by 403 publications

References 58 publications

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

The Assessment of Landscape and Environmental Risks and Hazards Caused by Landslides in Mountain Areas

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