Abstract
In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.
Highlights
Landslides are one of the most frequently occurring natural disasters that cause significant human casualties and infrastructure destruction
The final weights of landslide conditioning factors were calculated using an ensemble of Frequency Ratio (FR)-evidence belief functions (EBF), and utilised to create the final landslide susceptibility mapping (LSM)
Topographical features derived from digital elevation models such as Elevation, Slope, aspect, Plan curvature, Profile curvature, topographical wetness index (TWI), topographical position index (TPI), topographical roughness index (TRI)
Summary
Landslides are one of the most frequently occurring natural disasters that cause significant human casualties and infrastructure destruction. Landslides are triggered by several natural and man-made triggering events such as earthquakes, volcanic eruptions, heavy rains, extreme winds, and unsustainable construction activities such as informal settlement development and cutting of roads along the slopes (Glade et al, 2006; van Westen et al, 2008). Extreme meteorological events such as the Vaia storm of 2018 triggered landslides and debris flow, destroyed critical infrastructures in the northern parts of Italy (Boretto et al, 2021). Studies by (Rossi et al, 2019) estimated that approximately 2500 people were killed between 1945-1990. Predictive modelling of the Italian population at risk to landslides (Rossi et al, 2019) shows massive tendency of risk to the population with data acquired between 1861-2015, emphasizing the necessity of landslide risk studies
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