Two models for evaluating landslide hazards

Davis, John C.; Chung, Chang-Jo; Ohlmacher, Gregory C.

doi:10.1016/j.cageo.2006.02.006

Cited by 36 publications

(22 citation statements)

References 7 publications

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“…In the case of landslide susceptibility mapping, the aim of logistic regression is to find the best-fitting model to describe the relationship of the presence or absence of landslides (dependent variable) with a set of independent parameters such as slope angle, aspect, curvature, lithology and land use (Dai et al 2001, Ohlmacher and Davis 2003, Davis et al 2006, Lee and Pradhan 2007, Nefeslioglu et al 2008, Pradhan et al 2008, Yilmaz 2009). Quantitatively, the relationship between the probability of land sliding and the independent variables can be expressed as:…”

Section: Landslide Susceptibility Modelling Using Logistic Regressionmentioning

confidence: 99%

“…Quantitative methods are based on numerical statements of the correlation between causative factors and landslides. Deterministic models (Gokceoglu and Aksoy 1996) (Baeza andCorominas 2001, Carrara et al 2003), artificial neural network (Gomez and Kavzoglu 2005, Nefeslioglu et al 2008, Pradhan and Lee 2009a,b, Yilmaz 2009), fuzzy logic (Ercanoglu and Gokceoglu 2004, Kanungo et al 2006, Pradhan et al 2009, and logistic regression that is the most common statistical method used in earth sciences (Jade and Sarkar 1993, Wieczorek et al 1996, Guzzetti et al 1999, Dai et al 2001, Dai and Lee 2002, Ohlmacher and Davis 2003, Chau et al 2004a, Can et al 2005, Chao and Chan 2005, Davis et al 2006, Duman et al 2006, Greco et al 2007, Lee and Pradhan 2007, Nefeslioglu et al 2008, Pradhan et al 2008, Yilmaz 2009 (Kelarestaghi and Jafarian 2010), which makes hill slopes susceptible to landslide occurrence (Kelarestaghi and Garaee 2007), it is extremely necessary to model and map landslide spatial distribution using new quantitative approaches. The present study is carried out by identifying some main physical factors contributing to the landslide occurrence and incorporating them in logistic multiple regression, by which regional slope instability in the Sajarood basin, Northern Iran, was modelled.…”

Section: Introductionmentioning

confidence: 99%

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GIS-based spatial prediction of landslide susceptibility using logistic regression model

Mousavi

Kavian

Soleimani

et al. 2011

Geomatics, Natural Hazards and Risk

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In the present study, logistic regression analysis has been used to create a landslide hazard map for Sajarood basin, Northern Iran. At first, an inventory map of 95 landslides was used to produce a dependent variable, a value of 0 for absence and 1 for presence of landslides. The effect of causative parameters on landslide occurrence was assessed by the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients shows that the road network plays the major role in determining landslide occurrence. Elevation, slope curvature, rainfall and distance to fault were excluded from the final analysis, because these variables do not significantly add to the predictive power of the logistic regression. After running the final probability function into Arc/view 3.2 software, a landslide susceptibility map has been produced. The accuracy assessment shows an overall accuracy of the landslide susceptibility map to be 85.3%. An area of 53.01% is found to be located in a very low, 18.33% in low, 20.96% in moderate and 7.7% in high-risk regions. The proposed susceptibility map was tested using -2LL, Cox and Snell R 2 , Nagelkerk R 2 and Roc procedure, and it is found to be very reliable.

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Section: Landslide Susceptibility Modelling Using Logistic Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GIS-based spatial prediction of landslide susceptibility using logistic regression model

Mousavi

Kavian

Soleimani

et al. 2011

Geomatics, Natural Hazards and Risk

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“…To assess landslide susceptibility on a regional scale, multivariate statistical approaches were commonly used in the last decades. Especially discriminant analysis (Carrara et al, 1991(Carrara et al, , 2003Davis et al, 2006;Santacana et al, 2003), logistic regression Dai and Lee, 2002;Ohlmacher and Davis, 2003;Tasser et al, 2003;Van Den Eeckhaut et al, 2006;Yesilnacar and Topal, 2005) and neural networks (Ermini et al, 2005;Yesilnacar and Topal, 2005;Kanungo et al, 2006;Gomez and Kavzoglu, 2005) have successfully been applied.…”

Section: Introductionmentioning

confidence: 99%

On the influence of temporal change on the validity of landslide susceptibility maps

Meusburger

Alewell

2009

Nat. Hazards Earth Syst. Sci.

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Abstract. The consideration of non-stationary landslide causal factors in statistical landslide susceptibility assessments is still problematic. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. In this case study in the Central Swiss Alps, we aim to evaluate the effect of dynamic change of landslide causal factors on the validity of landslide susceptibility maps. Logistic regression models were produced for two points in time, 1959 and 2000. Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of the change of dynamic causal factors on the landslide probability. The deviation map explained 85% of new landslides occurring after 2000. We believe it to be a suitable tool to add a time element to the susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions.

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“…Such cal cu la tion, for time-based par ti tion ing of past landslides, used to be per formed for long-term sce nar ios (e.g., Chung and Fabbri, 2003;Da vis et al, 2006;Dewitte et al, 2006) with an as sump tion that let say for 20-40 year pe riod caus ative fac tors and land slide oc cur rences (af fected ar eas) rep re sent av er age con di tions.…”

Section: Fig 4 Density Distribution Of Landslide and Non-landslide mentioning

confidence: 99%

Landslide susceptibility and risk assessment in a non-mountainous region – a case study of Koronowo, northern Poland

Mrozek

Laskowicz

Zabuski

et al. 2016

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1 Pol ish Geo log i cal In sti tute-Na tional Re search In sti tute, Carpathian Branch, Skrzatów 1, 31-560 Kraków, Po land 2 Pol ish Acad emy of Sci ences, In sti tute of Hy dro-En gi neer ing, Kooecierska 7, 80-328 Gdañsk, Po land Mrozek, T., Laskowicz, I., Zabuski, L., Kulczykowski, M., OEwidziñski, W., 2016. Land slide sus cep ti bil ity and risk as sess ment in a non-moun tain ous re gion -a case study of Koronowo, north ern Po land. Geo log i cal Quar terly, 60 (3): 758-769, doi: 10.7306/gq.1307The study deals with land slide threats in a low-re lief re gion which ex em pli fies an area rarely per ceived as prone to such geohazards. Ac tu ally, in the gently un du lated land scape in the vi cin ity of Koronowo at the Brda River (South Pom er a nian Lake Dis trict, north ern Po land) in ten si fied landsliding was ob served in the re cent years. The field map ping and ex am i na tion of air pho tos showed that en dan gered ter rains are fairly ex ten sive and can not be lim ited to ini tially iden ti fied slope fail ure in cidences (hot-spots). The de vised land slide sus cep ti bil ity and haz ard map is an out come of pre dic tive mod el ling us ing em pir ical like li hood ra tio func tion (LR) with re spect to seven ev i den tial lay ers: el e va tion, slope an gle, slope as pect, flow ac cu mu la tion, sur face de pos its, depth to gla cial raft, land cover as well as the land slide scars. The val ues of po ten tial losses were cal cu lated based on the mapped land-use cat e go ries and cur rent mar ket prices of es tates and ser vices. The fi nal map, which re sulted from com bin ing land slide haz ard with po ten tial losses, shows dam age pro pen sity in a spa tial scale of the town sur round ings. It is meant as a sup port ive tool for de ci sion-mak ing with re gard to al lo cat ing funds for sta bi li za tion measures or plan ning place ment of new in vest ments. On this back ground, sta bi li za tion so lu tions for se lected sites are as sessed in a cost-ben e fit con text.Key words: land slide haz ard, risk eval u a tion, non-moun tain ous land scape, land-use plans, Brda val ley.

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Two models for evaluating landslide hazards

Cited by 36 publications

References 7 publications

GIS-based spatial prediction of landslide susceptibility using logistic regression model

GIS-based spatial prediction of landslide susceptibility using logistic regression model

On the influence of temporal change on the validity of landslide susceptibility maps

Landslide susceptibility and risk assessment in a non-mountainous region – a case study of Koronowo, northern Poland

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