Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Pradhan, Biswajeet; Lee, Saro

doi:10.1016/j.envsoft.2009.10.016

Cited by 845 publications

(340 citation statements)

References 51 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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“…The statistical approach, instead, is founded on the multivariate relationships between causal factors and past and present landslide occurrence. The multivariate relationships are often identified through conditional analysis (BonhamCarter et al, 1989;Carrara et al, 1995), discriminant analysis (Agterberg, 1974;Carrara, 1983;Carrara et al, 1995Carrara et al, , 2003Baeza and Corominas, 2001), linear or logistic regression (Atkinson and Massari, 1998;Guzzetti et al, 1999, and references therein; Gorsevski et al, 2000;Dai and Lee, 2003;Ohlmacher and Davis, 2003;Ayalew and Yamagishi, 2005), and artificial neural networks (Aleotti et al, 1996;Lee et al, 2001;Wang and Sassa, 2006;Falaschi et al, 2009;Pradhan and Lee, 2010).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

Vergari

Seta

Monte

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy), where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor maps. Finally, a validation procedure was applied based on the partition of the landslide inventory from multi-temporal aerial photo interpretation.Although encompassing some sources of uncertainties, the applied susceptibility assessment method provided a satisfactory and unbiased prediction for the Upper Orcia Valley. The results confirmed the efficiency of the selection procedure, as an unbiased step of the landslide susceptibility evaluation. Furthermore, we achieved the purpose of presenting a conceptually simple but, at the same time, effective statistical procedure for susceptibility analysis to be used as well by decision makers in land management.

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“…Eventually, the landslide susceptibility value is calculated by the following equation: S " p1´W r qˆA`W rˆN (15) where S is the eventual landslide susceptibility value, A is the eight-factor assessment value of landslide susceptibility, W r is the weight of the annual precipitation calculated by AHP, and N i is the annual precipitation normalization value of the six zones, respectively.…”

Section: Data Processingmentioning

confidence: 99%

“…Some researchers have recently applied different classification methods to LSM prediction. Many statistical methods exist for LSM, such as the logistic regression (LR) method [13][14][15][16][17], statistical index (SI) [18,19], discriminant analysis (DA) [20] and bivariate statistical analysis (BSA) [21]. However, the statistical approaches have very strict mathematical reasoning, and their application must meet strict demands.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the prediction samples usually cannot pass the hypothesis test in the assessment process. Some other approaches have also been developed, according to the methodologies of decision tree (DT) [22], genetic algorithm (GA) [23], artificial neural network (ANN) [15,[24][25][26][27][28][29], and support vector machine (SVM) [30][31][32][33]. These objective statistical methods were used for evaluating the relationships between various influencing factors and landslide inventories.…”

Section: Introductionmentioning

confidence: 99%

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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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Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Cited by 845 publications

References 51 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 assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

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

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