Morphometry and Debris-Flow Susceptibility Map in Mountain Drainage Basins of the Vallo di Diano, Southern Italy

Abstract

In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

“…Flow-R, a distributed empirical model, was implemented with great success not only in this study but also in many studies in the literature. Giano et al [2], Polat and Erik [60], Charbel and El Hage Hassan [50], and Jiang et al [51] discussed whether Flow-R and observed landslide runout results are compatible. The researchers concluded that Flow-R results are well matched with the observed results.…”

“…Applying a machine learning algorithm was advantageous in this study during the preparation of a susceptibility map to obtain quite reliable shallow landslide initiations. Flow-R software can also be implemented to distinguish landslide initiations by applying a grid-based overlay approach [2,50,51,54]. However, the pre-determination of landslide initiations via machine learning is thought to have a positive effect, increasing the success of the analyses with respect to the accuracy of the runout distance probability.…”

“…The value of dh is also a significant parameter that affects results by changing the reduced rate of roughness. The value of 1 was suggested for dh in recent studies [2,49,53]. Horton et al [105] also proposed that dh can be equal to 1 for both debris flows and shallow landslides.…”

“…Considering the increase in extreme rainfalls in the last decade due to climate change, it is possible to claim that shallow landslides are like any other type of natural hazard and that researchers should increasingly focus on them [1]. Runout distance determination in shallow landslide analysis is crucial because hazard evaluations involving runout distance information constitute one of the bases of sustainable development for societies living in mountainous areas [2]. Runout methods are yielding extraordinary insights into landslide runout analysis.…”

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

“…The development and distribution of geological disasters are closely related to geological and environmental conditions, topography and geomorphology, meteorology and hydrology, vegetation conditions, and human engineering activities (Giano et al 2021). Debris-flow is a kind of sudden geological disaster, with the characteristics of fast movement and short duration (Liu et al 2019).…”