Geomorfologia ambientale e applicata

Coordinatore: Edoardo Rotigliano

Il GdL di Geomorfologia ambientale ed applicata si è dato come obiettivo principale la messa a
punto di un protocollo per l’analisi multilivello (1:50.000 – 1:10.000) della
suscettibilità/pericolosità da frana e per la trasposizione cartografica dei relativi modelli previsionali
secondo modalità integrabili e codificabili all’interno degli attuali PAI.
Il GdL intende sfruttare una rete di aree pilota distribuite su scala nazionale per condurre una serie
di test metodologici comparativi, relativamente agli elementi chiave necessari alla modellazione:
unità di mappatura, fattori di controllo, inventari frane e aree diagnostiche, strategia di costruzione e
validazione dei modelli, tecniche statistiche di modellazione e indicatori di accuratezza/precisione,
adeguatezza geomorfologica dei modelli, relazione trigger/suscettibilità su scenari sismo- o pluvio-
indotti, layout cartografici e criteri di riclassificazione della probabilità/magnitudo.
Il GdL di Geomorfologia ambientale ed applicata mira ad omogeneizzare e standardizzare
metodologie di studio della suscettibilità/pericolosità da frana che possano essere assimilati dalle
amministrazioni locali per una corretta pianificazione territoriale e gestione del territorio in caso di

The aim of the research was to verify and compare the predictive power of different diagnostic areas in assessing landslide susceptibility with a multivariate approach. Scarps, landslide areas (the union between scarp and accumulation zones) and areas uphill from crowns, for rotational slides, source or scarp areas and landslide areas, for flows, have been tested. A multivariate approach was applied to assess the landslide susceptibility on the basis of three selected conditioning factors (lithology, slope angle, and topographic wetness index), which were combined in a Unique Condition Unit (UCU) layer. By intersecting the UCU layer with the vector layer of the diagnostic areas, landslide susceptibility models were produced, in which the susceptibility is assigned to each UCUs on the basis of the computed density function. In order to test the effects produced by selecting different diagnostic areas in the performance of the susceptibility models, validation procedures have been applied to evaluate and compare the performances of the derived predictive models. The validation results are estimated by comparing the prediction and the success rate curves, exploiting three morphometric indexes. A test area, the Guddemi river basin, was selected in the northern Sicilian Apennines chain, having a total area of nearly 25 km2 and being mainly characterized by the outcropping of clays, calcilutites, and marly limestones. Aerial analysis, integrated with a field survey, resulted in the recognition of 111 earth-flow and 145 earth-rotational slide landslides. Scarps, for rotational slides, and both source and landslide areas, for flows, produced very satisfactory validation results. For rotational slides, areas uphill from crowns and landslide areas are both responsible for lower predictive performances, characterized by validation curves close to being flat shaped, due to their incapability of identifying specific slope (UCU) conditions. Moreover, because of their limited size, the areas uphill from crowns seem to suffer from a relevant geostatistical “instability”, when a splitting is performed to produce the validation domains, so that an enhanced shift between success and prediction rate curves is produced. By comparing the relative susceptibility maps, the research allowed us to evaluate the key role played by the selection of the diagnostic areas; the validation of the models is proposed as a tool to quantify such differences in terms of predictive performance.

In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model.

A correct landslide hazard assessment (LHA) is fundamental for any purpose of territorial planning. In Italy, the methods currently in use to achieve this objective alternate between those based on mainly qualitative (geomorphological) and quantitative (statistical–numerical) approaches. The present study contributes to the evaluation of the best procedure to be implemented for LHA, comparing the results obtained using two different approaches (geomorphological and numerical) in a territorial context characterized by conditioning and triggering factors, favorable to the instability of the slopes. The results obtained, although preliminary, evidence the respective limitations of the methods and demonstrate how a combined approach can certainly provide mutual advantages, by addressing the choice of the best numerical model through direct observations and surveys.

Every year, institutions spend a large amount of resources to solve emergencies generated by hydrogeological instability. The identification of areas potentially subject to hydrogeological risks could allow for more effective prevention. Therefore, the main aim of this research was to assess the susceptibility of territories where no instability phenomena have ever been detected. In order to obtain this type of result, statistical assessments of the problem cannot be ignored. In this case, it was chosen to analyse the susceptibility to landslide using a flexible method that is attracting great interest in the international scientific community, namely the Weight of Evidence (WoE). This model-building procedure, for calculating landslide susceptibility, used Geographic Information Systems (GIS) software by means of mathematical operations between rasters and took into account parameters such as geology, acclivity, land use, average annual precipitation and extreme precipitation events. Thus, this innovative research links landslide susceptibility with triggering factors such as extreme precipitation. The resulting map showed a low weight of precipitation in identifying the areas most susceptible to landslides, although all the parameters included contributed to a more accurate estimate, which is necessary to preserve human life, buildings, heritage and any productive activity.