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

Abstract

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.

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

“…Since all of these methods are data-driven methods, the resulting models also largely depend on the type and quality of the input data, although this aspect generally receives less attention. However, several publications also discuss the influence of the type and quality of the used landslide inventory information on the results of susceptibility mapping [8,[13][14][15].…”

The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements

“…The study area is described in more detail in Steger et al (2016b) while additional information on the prevalent landslide processes and the litho-morphological characteristics of the area and its surroundings can be found in Schwenk (1992), Wessely et al (2006) and Petschko et al (2016).…”

“…2) were adopted from previous analyses (cf. Steger et al 2016b). Within this study, the expression real data relates to real world data from Lower Austria while the expression synthetic data refers to artificially generated data sets.…”

“…While a previous study examined the effects of positional accuracy in detail (cf. Steger et al 2016b), the present follow-up study devotes particular attention to the second quality-defining criterion, namely the completeness of a landslide inventory.…”

Research on the Geological Disaster Forecast and Early Warning Model Based on the Optimal Combination Weighing Law and Extension Method: a Case Study in China

“…There has been significant growth in the investigation of both the early warning models and early warning zonation mapping. Various methods have been proposed by many different scholars [10][11][12][13][14][15][16][17]. These methods fall into three broad categories: statistical, soft computing, and analytical.…”