Spatial statistical modeling of shallow landslides—Validating predictions for different landslide inventories and rainfall events

209

129

Abstract. Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, several aspects of the quality of the landslide susceptibility model and the resulting classified map are of high interest. In this study of landslides in Lower Austria, we focus on the model form uncertainty to assess the quality of a flexible statistical modelling technique, the generalized additive model (GAM). The study area (15 850 km 2 ) is divided into 16 modelling domains based on lithology classes. A model representing the entire study area is constructed by combining these models. The performances of the models are assessed using repeated k-fold cross-validation with spatial and random subsampling. This reflects the variability of performance estimates arising from sampling variation. Measures of spatial transferability and thematic consistency are applied to empirically assess model quality. We also analyse and visualize the implications of spatially varying prediction uncertainties regarding the susceptibility map classes by taking into account the confidence intervals of model predictions. The 95 % confidence limits fall within the same susceptibility class in 85 % of the study area. Overall, this study contributes to advancing open communication and assessment of model quality related to statistical landslide susceptibility models.

Section: H Petschko Et Al: Assessing the Quality Of Landslide Suscementioning

confidence: 99%

Assessing the quality of landslide susceptibility maps – case study Lower Austria

Petschko

Brenning

Bell

et al. 2014

209

129

“…A third scheme, frequently adopted for model spatial transferability or exportation (e.g. Von Ruette et al, 2011;Costanzo et al, 2012a;Lombardo et al, 2014;Petschko et al, 2014), is based on the adoption of two different catchments or areas for calibration and validation (spatial partition).…”

Section: Introductionmentioning

confidence: 99%

“…Von Ruette et al, 2011;Tseng et al, 2015). In particular, the application of specific validation strategies to evaluate the effect of the trigger phenomena in modifying the predictive performance of the models is very rare.…”

Section: Introductionmentioning

confidence: 99%

Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

Cama

Lombardo

Conoscenti

et al. 2015

Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios.The study area is the small catchment of the Itala torrent (10 km 2 ), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chronovalidation, based on the time partition of the landslide in- Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chronovalidated models. These are interpreted as effects of the nonlinearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

“…The first problem to solve is the scale of analysis; many authors prefer a regional-scale approach, which necessarily leads to a qualitative or semi-quantitative analysis due to the impossibility of obtaining or considering data from every landslide (Von Ruette et al, 2011). In this kind of approach, the probability that a landslide could cause damage is accounted for through hazard or susceptibility maps, which are the start point of the analysis.…”

Section: Introductionmentioning

confidence: 99%

Detailed and large-scale cost/benefit analyses of landslide prevention vs. post-event actions

Salbego

Floris

Busnardo

et al. 2015

Abstract. The main aim of this paper is to test economic benefits of landslide prevention measures vs. post-event emergency actions. To this end, detailed-and large-scale analyses were performed in a training area located in the northeastern Italian pre-Alps that was hit by an exceptional rainfall event occurred in November 2010. On the detailed scale, a landslide reactivated after 2010 event was investigated. Numerical modeling demonstrated that remedial works carried out after the landslide -water-removal intervention such as a drainage trench -could have improved slope stability if applied before its occurrence. Then, a cost/benefit analysis was employed. It defined that prevention would have been economically convenient compared to a non-preventive and passive attitude, allowing a 30 % saving relative to total costs. On the large scale, one of the most affected areas after 2010 event was considered. A susceptibility analysis was performed using a simple probabilistic model, which allowed to highlight the main landslide conditioning factors and the most hazardous and vulnerable sectors. In particular, such low-cost analysis demonstrated that almost 50 % of landslides occurred after 2010 event could be foreseen and allowed to roughly quantify benefits from regional landslide prevention. However, a large-scale approach is insufficient to carry out a quantitative cost/benefit analysis, for which a detailed case-by-case risk assessment is needed. The here proposed approaches could be used as a means of preventive soil protection in not only the investigated case study but also all those hazardous areas where preventive measures are needed.