A global landslide catalog for hazard applications: method, results, and limitations

Kirschbaum, Dalia; Adler, Robert F.; Hong, Yang; Hill, S. Gage; Lerner-Lam, A.

doi:10.1007/s11069-009-9401-4

Cited by 387 publications

(295 citation statements)

References 20 publications

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“…This paper uses global landslide inventory data from a combined database. This database stores landslide information from two existing inventories: the World Geological Hazard Inventory created by the Academy of Disaster Reduction and Emergency Management of Beijing Normal University (ADREM, BNU), and the NASA global landslide inventory (refer to Kirschbaum et al, 2010 for details). The NASA global landslide inventory mainly collects landslides from several existing databases, including the International Consortium on Landslides website (ICL; http://iclhq.org); International Landslide Centre, University of Durham (ILC; http: //www.landslidecentre.org); The EM-DAT International Disaster Database (http://www.emdat.be/); International Federation of Red Cross and Red Crescent Societies field reports (http://www.ifrc.org); Reliefweb (http://reliefweb.int); humanitarian disaster information run by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA); other online regional and national newspaper articles and media sources.…”

Section: Dependent Variablesmentioning

confidence: 99%

Landslide susceptibility mapping on a global scale using the method of logistic regression

Lin

Wang

2017

Nat. Hazards Earth Syst. Sci.

121

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Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

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Section: Dependent Variablesmentioning

confidence: 99%

Landslide susceptibility mapping on a global scale using the method of logistic regression

Lin

Wang

2017

Nat. Hazards Earth Syst. Sci.

121

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“…The accurate prediction of reservoir landslide processes is an important basis for early prevention, and it can reduce the loss of property and lives (Corominas et al, 2005). Therefore, geological surveying, monitoring, landslide prevention and landslide prediction must be improved to minimize the losses caused by landslides (Kirschbaum et al, 2010;Miyagi et al, 2011;Ahmed, 2013). A landslide can be regarded as a nonlinear and dynamic system that is affected by external factors, such as rainfall, reservoir water levels and groundwater.…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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“…Google Earth search and image interpretation were supported by secondary information. Similar method was used by investigators in their studies [23][24][25].…”

Section: Methodsmentioning

confidence: 99%

Review of Spatial and Temporal Distribution of Landslides in Tanzania

Ja¹,

Kervyn²

2017

J Ecosyst Ecogr

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This paper intended to inventorize the occurrence of landslides in Tanzania in temporal and spatial terms. Specifically, the inventory focused on identifying areas in Tanzania which are prone to landslides, identifying the frequency of landslide occurrence in the country, identifying the underlying and triggering factors for landslides, the socio-economic impacts of landslides and assessing the people's perception of landslides in Tanzania. Identification and mapping of landslide was achieved through the use primary and secondary sources of information. Primary data was acquired through exploratory field visit to the 5 landslide scars in the north and south Pare Mountains in the northern highlands of Tanzania. Short discussion with local people in the areas was made to gain insights into their perception of landslides. Secondary data was acquired through the review of achieved sources. This review was made in respect of the landslides that were not covered by the aforementioned exploratory field visit. The EM-DAT was also used to provide information on the number of landslide events, fatalities and socio-economic impacts. Google Earth™ search and image interpretation were used to located and describe landslides in other regions of the country that were not covered by the aforementioned exploratory field study. A 90 m resolution Digital Elevation Model (DEM) from USGS for Tanzania was used to map the landslide scars identified through Google Earth search. Google Earth search and image interpretation were supported by secondary information from archived sources. The inventory noted that landslides in Tanzania have received relatively limited attention both in the literature and in government disaster management plans. With exception of studies conducted in Uluguru Mountain in 1970s, no formal landslide research has been made in recent years. Despite extensive web searches, no landslide inventory has been made in Tanzania. This may presumably be due to existing assumption that landslides in Tanzania have low frequency of occurrence and are localized compared to other disasters such as floods, epidemics and drought, whose occurrence affect many more people, drawing more community, government and media attention. This has thus, affected the way landslide events are reported and managed in the country. This assertion however, does not preclude the fact that one/few landslide events, depending on their magnitude, where they occur and nature of elements at risk, can affect significant number of people and vice versa. This inventory has also documented cultural perspectives on landslides. Care must be taken to incorporate appropriate indigenous knowledge and people's perception pertaining to landslides and other disasters as these are critical in designing and implementing culturallyinformed and low cost disaster management strategies. This may, in turn, obviate the need or reduce cost for early warning systems. Landslide information provided by some reviewed sources is limited in time and space. Most of the visi...

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A global landslide catalog for hazard applications: method, results, and limitations

Cited by 387 publications

References 20 publications

Landslide susceptibility mapping on a global scale using the method of logistic regression

Landslide susceptibility mapping on a global scale using the method of logistic regression

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Review of Spatial and Temporal Distribution of Landslides in Tanzania

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