Suchergebnisse

Abstract. We characterize and compare creep preceding and following
the complex 2011 Pampahasi landslide (∼40 Mm3±50 %) in the city of La Paz, Bolivia, using spaceborne radar
interferometry (InSAR) that combines displacement records from both
distributed and point scatterers. The failure remobilized deposits of an
ancient complex landslide in weakly cemented, predominantly fine-grained
sediments and affected ∼1.5 km2 of suburban development.
During the 30 months preceding failure, about half of the toe area was
creeping at 3–8 cm a−1
and localized parts of the scarp area showed
displacements of up to 14 cm a−1. Changes in deformation in the 10 months
following the landslide demonstrate an increase in slope activity and
indicate that stress redistribution resulting from the discrete failure
decreased stability of parts of the slope. During that period, most of the
landslide toe and areas near the head scarp accelerated, respectively, to
4–14 and 14 cm a−1. The extent of deformation increased to cover most, or
probably all, of the 2011 landslide as well as adjacent parts of the slope
and plateau above. The InSAR-measured displacement patterns, supplemented by
field observations and optical satellite images, reveal complex slope
activity; kinematically complex, steady-state creep along pre-existing
sliding surfaces accelerated in response to heavy rainfall, after which
slightly faster and expanded steady creeping was re-established. This case
study demonstrates that high-quality ground-surface motion fields derived
using spaceborne InSAR can help to characterize creep mechanisms, quantify
spatial and temporal patterns of slope activity, and identify isolated
small-scale instabilities; such details are especially useful where
knowledge of landslide extent and activity is limited. Characterizing slope
activity before, during, and after the 2011 Pampahasi landslide is
particularly important for understanding landslide hazard in La Paz, half of
which is underlain by similar large paleolandslides.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk. ; Validerad;2020;Nivå 2;2020-04-27 (johcin)