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In the recent years advanced statistical methods and GIS tools have been frequently used to assess landslide susceptibility. The latter is estimated by establishing statistical relationships between landscape characteristics and spatial distribution of past slope-failures. These are mapped mainly by recognizing changes on slope morphology produced by gravity. Despite of this, most of the researches on landslide susceptibility do not consider that slope-failures modify topography and associate high probability of landsliding with topographic characteristics that differ from those that led slope-failures. In this research we analyzed landslide susceptibility in the basin of the Malvello river with two statistical methods: binary logistic regression and multivariate adaptive regression splines. The study area, which extends for 51 km2 in western Sicily (Italy), is characterized by large outcroppings of clays and marls and is severely affected by shallow landslides. Bedrock lithology and land use were included as predictive variables, in addition to a set of primary and secondary topographic attributes. The latter were derived from a digital elevation model where altitude of areas hosting landslides was interpolated from adjacent undisturbed portions of the slopes. We assume that these artificial surfaces represent the old topography more efficiently respect to the morphology of depletion and accumulation zones of landslides. Ten random samples, with the same number of positive and negative cases, were used to train and test the susceptibility models. Accuracy, as well as overfitting and robustness of the models, were evaluated by drawing receiver operating characteristic (ROC) curves and calculating the area under the ROC curve. In addition of comparing the performance of the statistical methods, the validation results allowed us to highlight advantages/drawbacks of reconstructing original topography of slope-failures when mapping landslide susceptibility.

A GIS-analysis was carried out in a test basin of southern Sicily, the Magazzolo River basin, in order to assess susceptibility conditions to gully erosion phenomena. The linear density of ephemeral and permanent gullies computed within each class of nine environmental variables was used to generate a gully erosion susceptibility map for the area. A validation procedure carried out in order to test the reliability of the adopted method highlighted a clear correlation between the occurrence of gullies and the computed susceptibility levels

A seguito di un evento estremo di pioggia verificatosi il 1 ottobre 2009 in un settore circoscritto del versante ionico della Provincia di Messina, si sono sviluppati numerosi eventi franosi classificabili per la maggior parte come colate di fango e detrito a rapida evoluzione (debris-mud flows). Dai primi sopralluoghi effettuati, in assenza ancora di un censimento sistematico, si sono riconosciuti eventi in numero di oltre 500, che hanno interessato sia nei versanti interni dei bacini idrografici che sversano nello Ionio, sia sui rilievi direttamente prospicienti la costa. L’elevato numero di eventi occorsi ha consentito di dettagliare dal punto di vista morfologico i fenomeni avvenuti e di compiere preliminari analisi statistiche relative alle aree di innesco in rapporto con parametri geomorfologici, morfometrici, litologici e dello spessore di copertura. I rilievi sono stati condotti direttamente sul terreno, dall’alto tramite sorvoli su elicotteri messi a disposizione dalla Protezione Civile Nazionale, da ortofoto del 2000 e del 2008, da immagini satellitari. I dati così acquisiti successivamente sono stati successivamente elaborati e digitalizzati al fine di redigere una “Carta di censimento preliminare delle aree di innesco”, rappresentate da punti, di seguito indicati, in analogia con il sistema di archiviazione IFFI, con il termine “PIFF”, con particolare riferimento alle aree nell’immediato intorno degli abitati coinvolti. Tali dati sono stati poi trasposti in ambiente GIS, al fine sia di realizzare un database continuamente aggiornabile, condizione di primaria importanza in ragione della estrema dinamicità di modellamento dei versanti, sia di disporre di un database che consentisse di passare dall’analisi dei fenomeni all’analisi della pericolosità. In tal senso sono state elaborate le prime analisi statistico-descrittive relative ai 113 fenomeni censiti la cui distribuzione spaziale è stata messa in relazione con la variabilità geografica di parametri geologici, morfometrici, morfologici e di uso del suolo. Vengono inoltre espresse alcune considerazioni preliminari circa le soglie di innesco dei fenomeni.

In the studies of landslide susceptibility assessment, which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on 7 th and 8 th November 2009, in an area of about 26 km 2 close to the Caldera Ilopango, El Salvador (CA), was employed. Two validation strategies have been applied to both statistical techniques, thus obtaining four models – BLR (I), MARS (I), BLR (II) and MARS (II) – to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modelling procedure, 50 replicates were created for each model and standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among True Negatives (TNs).

Urban-geomorphology studies in historical cities provide a significant contribution towards the broad definition of the Anthropocene, perhaps even including its consideration as a new unit of geological time. Specific methodological approaches to recognize and map landforms in urban environments, where human-induced geomorphic processes have often overcome the natural ones, are proposed. This paper reports the results from, and comparison of, studies conducted in coastal historical cities facing the core of the Mediterranean Sea – that is, Genoa, Rome, Naples, Palermo (Italy) and Patras (Greece). Their settlements were facilitated by similar climatic and geographical contexts, with high grounds functional for defence, as well as by the availability of rocks useful as construction materials, which were excavated both in opencast and underground quarries. Over centuries, urbanization has also required the levelling of relief, which was performed by the excavation of heights, filling of depressions and by slope terracing. Consequently, highly modified hydrographic networks, whose streams were dammed, diverted, modified in a culvert or simply buried, characterize the selected cities. Their urban growth, which has been driven by maritime commercial activities, has determined anthropogenic coastal progradation through port and defence or waterfront works. Aggradation of artificial ground has also occurred as a consequence of repeated destruction because of both human and natural events, and subsequent reconstruction even over ruins, buried depressions and shallow cavities. As a result, the selected cities represent anthropogenic landscapes that have been predominately shaped by several human-driven processes, sometimes over centuries. Each landform represents the current result, often from multiple activities with opposing geomorphic effects. Beyond academic progress, we believe that detecting and mapping these landforms and processes should be compulsory, even in risk-assessment urban planning, because of the increase of both hazards and vulnerability as a result of climate-change-induced extreme events and extensive urbanization, respectively.

In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence.