Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

Domínguez-Cuesta, María José; Jiménez-Sánchez, Montserrat; Berrezueta, Edgar

doi:10.1016/j.geomorph.2007.01.004

Cited by 57 publications

(33 citation statements)

References 35 publications

(42 reference statements)

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“…Thus, we need to choose a set of reasonable and scientific factors which comprehensively reflect the landslide sensitivity objectively, scientifically, and systematically. Taking the Huoxi Coalfield of Shanxi Province as the research area, and according to regional characteristics and the experience of experts [32][33][34], the correlations were analyzed among the spatial distribution characteristics of landslides and the factors which affect the stability of slopes. Finally, choosing five categories (including the basic influence of landslide topography, geology, hydrology, land cover, and human activities) and 12 conditioning factors influencing landslides, especially the mining disturbance factor (Table 1), we explored the impact of mining activities on landslide disasters.…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Zhang

Zhao

et al. 2017

IJGI

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Abstract:Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0-0.02), medium (0.02-0.1), high (0.1-0.85), and very high (0.85-1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes.

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Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Zhang

Zhao

et al. 2017

IJGI

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“…Although landslides recorded in Asturias normally show reduced size and depth (metric to decametric), they often affect highly populated areas or the dense infrastructure network, causing every year relevant economic loses and sporadic human victims [34]. The BAPA (Base de datos de Argayos del Principado de Asturias-Principality of Asturias Landslide Database) gathers 2245 individual landslides recorded during the period 1980-2016 [35], showing the high incidence of those phenomena in the study area.…”

Section: Study Areamentioning

confidence: 99%

Meteorological Patterns Linked to Landslide Triggering in Asturias (NW Spain): A Preliminary Analysis

et al. 2018

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“…En el territorio del Macizo Asturiano se ha prestado especial atención a la relación entre lluvias intensas y movimientos en masa rápidos (Domínguez-Cuesta et al, 1999;Jiménez Sánchez et al, 1999;Domínguez-Cuesta et al, 2007). Los efectos de la fusión nival sobre los movimientos de ladera, se han abordado en el contexto de la alta montaña, más concretamente en el ámbito de los Picos de Europa (Serrano y González-Trueba, 2004;González-Trueba y Serrano, 2010;Ruiz-Fernández, 2015).…”

Section: Introductionunclassified

El episodio de movimientos en masa asociado a los temporales de nieve de 1888 en el Macizo Asturiano

García-Hernández

Fernández

Oliva

et al. 2018

BAGE

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<p>Este estudio analiza un episodio de movimientos en masa generado en el contexto de una serie de temporales cuyas repercusiones se dejaron sentir en numerosos países europeos e incluso en la Costa Este de Estados Unidos, afectando también al norte peninsular y, muy especialmente, al territorio del Macizo Asturiano. En este ámbito el episodio consistió en cuatro temporales de nieve que se sucedieron entre el 14 de febrero y el 8 de abril de 1888, dando lugar a espesores nivales que alcanzaron los 9 m de espesor máximo en núcleos como Pajares (1000 m s.n.m.). El manejo de fuentes históricas en combinación con el trabajo de campo y las entrevistas realizadas entre la población, ha permitido esclarecer las circunstancias en las que algunos de estos eventos se dieron, estableciendo la tipología de 27 de los movimientos desencadenados y determinando que, el deslizamiento, fue el tipo de inestabilidad más común. Determinados condicionantes como la litología del sustrato y el abrupto relieve propio de las zonas más altas del Macizo, unidos a factores antrópicos como las modificaciones previamente realizadas en la topografía de determinadas áreas, influyeron en el desarrollo de los hechos. Sin embargo, los aportes de agua procedentes de la fusión nival, se muestran como el principal agente causal de este episodio de movimientos en masa en el que 28 eventos generaron daños materiales así como grandes retrasos en las comunicaciones, resultando fallecida una persona.</p>

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Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

Cited by 57 publications

References 35 publications

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Meteorological Patterns Linked to Landslide Triggering in Asturias (NW Spain): A Preliminary Analysis

El episodio de movimientos en masa asociado a los temporales de nieve de 1888 en el Macizo Asturiano

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