Carlotta Bartelletti

Shallow landslides susceptibility assessment in different environments

ABSTRACT The spatial distribution of shallow landslides is strongly influenced by different climatic conditions and environmental settings. This makes difficult the implementation of an exhaustive monitoring technique for correctly assessing the landslide susceptibility in different environmental contexts. In this work, a unique methodological strategy, based on the statistical implementation of the generalized additive model (GAM), was performed. This method was used to investigate the shallow landslide predisposition of four sites with different geological, geomorphological and land-use characteristics: the Rio Frate and the Versa catchments (Southern Lombardy) and the Vernazza and the Pogliaschina catchments (Eastern Liguria). A good predictive overall accuracy was evaluated computing by the area under the ROC curve (AUROC), with values ranging from 0.76 to 0.82 and estimating the mean accuracy of the model (0.70–0.75). The method showed a high flexibility, which led to a good identification of the most significant predisposing factors for shallow landslide occurrence in the different investigated areas. In particular, detailed susceptibility maps were obtained, allowing to identify the shallow landslide prone areas. This methodology combined with the use of the rainfall thresholds for triggering shallow landslides may provide an innovative tool useful for the improvement of spatial planning and early warning systems.

The influence of geological–morphological and land use settings on shallow landslides in the Pogliaschina T. basin (northern Apennines, Italy)

ABSTRACT On 25 October 2011, the eastern Liguria (Vara Valley and Cinque Terre area) and northwestern Tuscany (Magra Valley) were affected by an extreme rainstorm (almost 600 mm/24 h) that caused floods, thousands of shallow landslides, 13 casualties and damage to villages and infrastructure. This study aims at analysing the main features of the 25 October 2011 shallow landslides occurred in the Pogliaschina Torrent basin (25 km2 wide, Vara Valley), in order to investigate the influence of specific predisposing factors (land use, geological and structural setting, plan and profile curvature, slope angle and aspect) on landslide occurrence. For this purpose, both a landslide inventory map and a geology map (scale 1:10,000) were prepared. In addition, a database including the main geological, geomorphological, structural and land use features of the landslide source areas was implemented. The relationship between landslide source areas and the main predisposing factors was evaluated through spatial and statistical analysis.

Shallow Landslides Triggered by the 25 October 2011 Extreme Rainfall in Eastern Liguria (Italy)

A very heavy rainstorm hit Eastern Liguria (Vara Valley and Cinque Terre area) and North-western Tuscany (Magra Valley) on 25 October 2011. This event produced floods and hundreds of shallow landslides, causing 13 casualties and severe damage to villages, infrastructures and roads. In the Vara Valley the Brugnato rain gauge recorded 468.8 mm in 6 h, with a maximum rainfall intensity of 143.4 mm/h. A landslide inventory map was carried out, together with a database including the main features of the source areas. At present, the database is complete for the Pogliaschina Torrent basin (Vara Valley), where at least 658 shallow landslides (mainly soil slip-debris flows and debris flows) were triggered. The shallow landslides induced by the 25 October 2011 event were analysed, together with geological, geomorphological and land use features of their source areas, with the aim of identifying common triggering conditions. This paper shows preliminary results of the comparison between the landslide inventory and the main slope features of the Pogliaschina Torrent basin.

Assessing shallow landslide susceptibility by using the SHALSTAB model in Eastern Liguria (Italy)

On 25 October 2011 a heavy rainstorm hit Eastern Liguria (Vara Valley and Cinque Terre) and North-western Tuscany (Magra Valley), causing floods and hundreds of shallow landslides.This study aims at assessing the shallow landslide susceptibility using the physically based model SHALSTAB (Shallow Landslide Stability Model) in the Pogliaschina Torrent basin (Vara Valley). The susceptibility map elaborated with SHALSTAB was compared with the landslide inventory map, which confirmed the good performance of this model for the study area.The implementation of the SHALSTAB model provided a preliminary shallow landslide susceptibility map of the Pogliaschina T. basin and quite promising results on the shallow landslide spatial prediction.

Developing and testing a data-driven methodology for shallow landslide susceptibility assessment: preliminary results

In this work a data-driven methodology for shallow landslide susceptibility assessment is presented. The procedure is based on the Generalized Additive Model (Hastie and Tibshirani, 1990) and it is developed to be applied in different contexts, using terrain attributes, land use and lithological data. The application of the method in three different contexts in Italy shows the good forecasting capability of the model. The implementation of this method allows for building landslide susceptibility maps, which are a fundamental basis in hazard and risk assessment.

Analysis of the Predisposing Factors for Different Landslide Types Using the Generalized Additive Model

In this paper, a semi-parametric nonlinear regression technique, known as Generalized Additive Model (GAM), was implemented for the landslide susceptibility assessment in the Gravegnola catchment (Northern Apennines, Eastern Liguria, Italy), which was affected by more than 500 shallow landslides on the 25 October 2011 intense rainfall event. Twelve explanatory variables derived from DEM with 5-m resolution, river network, land use and geological maps were considered to investigate their influence on landslide type occurrence. The predictive performance of different combinations of explanatory variables has been evaluated through a cross-validation technique and ROC curve analysis. Different susceptibility maps for each landslide type were finally produced and the results were compared. The preliminary results show the higher ability of GAM than a single regression technique in selecting the most influent predisposing factors on the basis of the type of movement involved in landsliding.

Remarks on the Role of Landslide Inventories in the Statistical Methods Used for the Landslide Susceptibility Assessment

Open image in new window In this work, the role of different landslide inventories in susceptibility assessment was evaluated using a non linear regression technique, namely the generalized additive model (GAM).The investigation was carried out in three study areas: the Versa catchment (Oltrepo Pavese, Southern Lombardy, Italy), the Vernazza catchment (Cinque Terre, Eastern Liguria, Italy) and the Pogliaschina catchment (Vara Valley, Eastern Liguria, Italy). Two landslide inventories related to the 2009 and 2013 rainfall events were taken into account in the Versa catchment, whereas two landslide inventories (referred to the same 2011 rainfall event) which differ for methods of detection and criteria adopted for the landslide mapping were considered in the Vernazza and Pogliaschina catchments. The predictive performance of GAM for each landslide inventory was evaluated. The results related to different inventories were compared. The results show that the predictive capability of the model and the landslide susceptibility are significantly influenced by the type of landslide inventory. Thus, the work highlights that a standard criterion for preparing inventories should be adopted in order to produce landslide susceptibility assessment as reliable as possible.

GIS-Based Deterministic and Statistical Modelling of Rainfall-Induced Landslides: A Comparative Study

Open image in new window In this paper three different approaches for landslide susceptibility modeling—Shallow Landslide Stability model (SHALSTAB), Likelihood Ratio (LR) and Generalized Additive Model (GAM)—are compared. They are based on deterministic and statistical methods, respectively. These methods were tested in the Pogliaschina catchment (25 km2 wide; Northern Apennines, Eastern Liguria, Italy), heavily hit by an intense rainfall on 25 October 2011, that caused hundreds of shallow landslides, human losses and severe damage to infrastructure and buildings. The paper focuses on the assessment of the predictive performance of the three methods through a two-fold cross-validation technique and prediction rate curves (PRCs) analysis. The preliminary results have revealed that statistical methods have a higher predictive capability than the deterministic one.

Deterministic and Probabilistic Slope Stability Models Forecast Performance at ~1:5000-Scale

Open image in new window Deterministic methods are appropriate for analyzing specific slopes at site-scale where geotechnical parameters are better known. Probabilistic techniques provide better results than deterministic methods at regional scales (1:10,000–1:50,000). However, the performances of deterministic and probabilistic methods at large scales (e.g. 1:5000-scale) are not well-known. We applied GIS-based deterministic (WEDGEFAIL, SAFETYFACTOR, SHALSTAB) and probabilistic (Likelihood ratio) methods to a mountain road of 14 km in the Alpujarras region (S Spain) to investigate the behavior of these models at detailed scales. The studied road stretch was affected by 111 landslides (7–8 landslides/km) during the 2009–2010 winter in a period of high precipitation. These landslides cut off the road in several points and disconnected the central region of Alpujarras from the main transport infrastructures. We delimited a small study area with only 4 km2 restricted to the slopes that cross the road where we gathered as much data as possible. Our results show that deterministic methods have less prediction capability at ~1:5000-scale than probabilistic methods and it seems that the needed effort to improve their results is not worthwhile. However, it must take into account that probabilistic methods need an inventory and they could not have been applied before the analyzed landslide event. As our results indicate, the deterministic methods, such as the SHALSTAB model, are reliable tools to make an evaluation of the stability of cut slopes in a roadway at project-scale.

Response to ‘comment on “The influence of geological–morphological and land use settings on shallow landslides in the Pogliaschina T. basin (northern Apennines, Italy)” by Bartelletti et al. (2017)’

Response to ‘comment on “The influence of geological–morphological and land use settings on shallow landslides in the Pogliaschina T. basin (northern Apennines, Italy)” by Bartelletti et al. (2017)’ Carlotta Bartelletti , Roberto Giannecchini , Giacomo D’Amato Avanzi , Yuri Galanti a and Alberto Mazzali Department of Earth Sciences, University of Pisa, Pisa, Italy; Magra River Basin Authority, Sarzana, Italy