Abstract
The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, Italy. Two landslide inventories were built thanks to detailed geomorphological mapping and aerial photograph analysis. The landslides were used to create the models before 1975, while those after 1975 were employed to validate the predictive power of the model. Possible landslide-related factors were chosen from a geomorphological survey. The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes. A comparison between the distribution of landslides after 1975 and those derived from models provided the predictive power of each model, which in turn was used to define the best predictive model. Reduced chi-square analysis allowed to define the efficiency of MSUE as dependent variable representation. MSUE can be applied as dependent variable representation to landslide susceptibility zonation with appreciable results. In the Roglio basin, slope angle, distance from streams, and from tectonic lineaments proved to be the main controlling factors of translational slides, whereas in the Milia basin, lithology and slope angle gave more satisfactory results as landslide-predisposing factors.
Highlights
If only social and economic parameters are considered when planning urban and industrial growth, the derived infrastructures may be threatened by natural phenomena, such as those of a geomorphological nature
The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, in southern-central and central Tuscany (Italy)
The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes
Summary
If only social and economic parameters are considered when planning urban and industrial growth, the derived infrastructures (e.g., buildings, roads, factories) may be threatened by natural phenomena, such as those of a geomorphological nature. The use of maps able to depict the spatial distribution of a natural hazard or susceptibility to its occurrence has become crucial for correct territorial planning, risk mitigation and management [1,2] In this regard, how to generate maps of landslide susceptibility is a key question, since landsliding is one of the most common sources of natural risk. For the final purpose of this study, the conditional analysis method has been applied to factor combinations [24], as it has fewer limitations than other systems of statistical analysis. This method does not require independence variables and covariate normal distribution. An analysis of reduced chi-square was performed to define the efficiency of MSUE as a dependent variable
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