Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

103

Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce.The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available.The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Crossvalidation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions.The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

Section: Misleading Performance Estimatesmentioning

confidence: 85%

Section: Misleading Performance Estimatesmentioning

confidence: 99%

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Steger

Brenning

Bell

et al. 2016

103

“…Logistic regression techniques have to be adapted to the specificities of landslide analysis, as landslides (like many other natural hazards) can be considered to be rare events (Demoulin and Chung, 2007). Rare events have occurrence frequencies that are low (Maalouf and Trafalis, 2011), with the number of events in the dataset dozens to thousands times smaller than the number of non-events.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression applied to natural hazards: rare event logistic regression with replications

Guns

Vanacker

2012

Abstract. Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, socalled rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

“…These predictions are based on the assumption that future landslide events will occur under similar preparatory conditions as past events. However, the performance of a susceptibility model relies on the quality of the landslide inventory, the modelling method and on the selection of variables representing preparatory conditions that are used to predict landslides (Demoulin and Chung, 2007). …”

Section: Introductionmentioning

confidence: 99%

“…The knowledge gained can be used to improve predictions wherein the set of preparatory conditions has a well-defined relationship to landslides (Hastie and Tibshirani, 1990;Demoulin and Chung, 2007). In general, models are used to form more effective forest management practices aimed at reducing damages caused by landslides.…”

Section: Introductionmentioning

confidence: 99%

Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada

Goetz

Guthrie

Brenning

2015

Abstract. Safe operations of forest practices in mountainous regions require effective development planning to mitigate hazards posed by landslides. British Columbia, Canada, has for the past 2 decades implemented landslide risk management policies aimed at reducing the impacts of the forestry industry on landslides. Consequently, it is required that timber harvesting sites be evaluated for their potential or existing impacts on terrain stability. Statistical landslide susceptibility modelling can enhance this evaluation by geographically highlighting potential hazardous areas. In addition, these statistical models can also improve our understanding of regional landslide controlling factors. The purpose of this research was to explore the regional effects of forest harvesting activities, topography, precipitation and geology on landslides initiated during an extreme rainfall event in November 2006 on Vancouver Island, British Columbia. These effects were analyzed with a nonparametric statistical method, the generalized additive model (GAM). Although topography was the strongest predictor of landslide initiation, low density forest interpreted as regrowth areas and proximity to forest service roads were jointly associated with a 6-to 9-fold increase in the odds of landslide initiation, while accounting for other environmental confounders. This result highlights the importance of continuing proper landslide risk management to control the effects of forest practices on landslide initiation.