A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models

Abstract

Distributed physically based slope stability models usually provide outputs representing, on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically sound, from an operational point of view has several limitations. First, the procedure of validation lacks standards. As instance, it is not straightforward to decide above which percentage of failure probability a pixel (or larger spatial units) should be considered unstable. Second, the validation procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap landslide inventories and model outputs to extract statistically significant performance metrics. Finally, if model outputs are conceived to be used in the operational management of landslide hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue warnings aimed at specific operational procedures. In this work, a tool is presented that automates the validation procedure for physically based distributed probabilistic slope stability models and translates the pixeled outputs in warnings released over larger spatial units like small watersheds. The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion on the basis of two threshold values—the probability of failure above which a pixel should be considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed in each watershed to consider the hazard level widespread enough to justify the issuing of an alert (instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare them with an existing landslide inventory; build a contingency matrix counting true positives, true negatives, false positive, and false negatives; and draw in a map the results of the validation. The DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different configurations tested, we performed a quantitative comparison to identify which criterion (in the case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a watershed) produces the most reliable validation results, thus appearing as the most promising candidate to be used to issue alerts during civil protection warning activities.

Towards a National-Scale Dataset of Geotechnical and Hydrological Soil Parameters for Shallow Landslide Modeling

“…Distributed physically based models represent the most rigorous and scientifically sound technique to model landslide occurrence in a given study area, as they use complex mathematical equations to account for all the physical processes involved in landslide triggering and mobilization [1]. Many models have been proposed to this aim [2][3][4][5][6][7][8][9][10], but despite this, their application over large areas is still rare and it is limited by several drawbacks. The main one is the limited availability, for large areas, of detailed information on geotechnical and hydraulic properties of soils (e.g., cohesion, internal friction angle, soil unit weight, hydraulic conductivity, and so on) [11][12][13].…”

Rainfall-Induced Landslide Susceptibility Assessment and the Establishment of Early Warning Techniques at Regional Scale

“…The integrated application of these tools, through the selection of appropriate failure probability thresholds (FPTs) and instability diffusion thresholds (IDTs), significantly improves the performance of early warning systems. This synergy provides scientific support for decision making by local governments and disaster response teams [19].…”

Advanced Technologies for Landslides—ATLaS (WCoE 2020–2023)

“…The raw outputs of the model, which consists of slope failure probability at the pixel level, are aggregated over larger spatial units (e.g., slope units or small basins) according to a criterium that is necessarily very site specific and dependent on the needs of the end-users. Therefore, a semi-automated tool has been developed to objectively identify the criterium that maximizes the correct predictions while minimizing the errors (Bulzinetti et al 2021). With these features, it has been possible to apply HIRESSS to several Italian test sites ranging from 18 km 2 to 3500 km 2 in areal extension, demonstrating a promising potential for a pre-operational use.…”