Shallow landslides triggered by consecutive rainfall events at Catanzaro strait (Calabria–Southern Italy)

Borrelli, Luigi; Cofone, Gino; Coscarelli, Roberto; Gullà, Giovanni

doi:10.1080/17445647.2014.943814

Cited by 32 publications

(28 citation statements)

References 33 publications

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“…Thus, a reliable and accurate LIM, which is more concordant with region reality, can obtain a reasonable result in landslide modeling. The locations of 111 shallow landslides were firstly obtained from the Forests, Rangeland and Watershed Management Organization of Iran (FRWOI), and were then checked using field surveys, interpretations of aerial photographs (1:40,000 scale) and Google Earth images [ 22 , 24 ]. In this study, two scenarios were used: (1) the combination of sample sizes of 60%/40% and 70%/30% with a resolution of 10 m; and (2) the combination of sample sizes of 80%/20% and 90%/10% with a resolution of 20 m. These scenarios were selected after changing different sample sizes and raster resolutions in this study.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Shirzadi

Soliamani

Habibnejhad

et al. 2018

Sensors

171

102

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The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Shirzadi

Soliamani

Habibnejhad

et al. 2018

Sensors

171

102

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“…In this experiment we employed Multivariate Adaptive Regression Splines (MARS; Friedman, 1991) to model the spatial distribution of landslides that were triggered in two study areas of Sicily number of studies have exploited Google Earth images to prepare landslide inventories (e.g., Costanzo et al, 2012a,b;Schicker and Moon, 2012;Van Den Eeckhaut et al, 2012;Borrelli et al, 2014;Zhang et al, 2015) and, as far as we know, none produced their susceptibility models without incorporating information from field surveys and/or other sources of data. Indeed, conventional methods to prepare landslide inventory rely mainly on geomorphological field mapping and on the interpretation of stereoscopic aerial photographs (Guzzetti et al, 2012).…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy

et al. 2016

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A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004-2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3 km2, respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5 m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Multivariate Adaptive Regression Splines (MARS) were applied to model landslide susceptibility whereas receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate model performance. To evaluate the robustness of the whole procedure, we prepared 10 different samples of positive (landslide presence) and negative (landslide absence) cases for each area. Absences were selected through two different methods: (i) extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas; and (ii) selection as randomly distributed individual grid cells. A comparison was also made between the predictive performances of models including and not including the lithology parameter.The models trained and tested on the same area demonstrated excellent to outstanding fit (AUC > 0.8). On the other hand, predictive skill decreases when measured outside the calibration area, although most of the landslides occur where susceptibility is high and the overall model performance is acceptable (AUC > 0.7). The results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis. Models whose absences were selected using random circles showed a significantly better performance when learning and validation samples were extracted from the same area; whereas, conversely, no significant difference was observed when testing the models outside the training area

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“…Numerous researches focusing on flooded area mapping, generally based on models or remotelysensed data (De Musso et al, 2018;Guerriero et al, 2018Guerriero et al, , 2020Romanescu et al, 2017;Segura-Beltrán et al, 2016), and on riverbed geomorphic response analysis and mapping (Fuller, 2008;Heritage et al, 2004;Hooke, 2016;Morche et al, 2007;Rinaldi et al, 2016;Thompson & Croke, 2013;Yousefi et al, 2018), were conducted worldwide to investigate high-magnitude floods dynamics. Furthermore, several studies analyzed ground effects triggered by intense rainfalls and floods specifically in mountain and hilly areas (Bartelletti et al, 2017;Borrelli et al, 2015;Cevasco et al, 2012Cevasco et al, , 2015Cevasco et al, , 2017Pepe et al, 2019aPepe et al, , 2019bRago et al, 2017;Santo et al, 2017;Tessitore et al, 2011). In contrast, relatively little research was carried out focusing on detailed field-based floodinduced geomorphic effects mapping in lowland areas (Magilligan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Flood-induced ground effects and flood-water dynamics for hydro-geomorphic hazard assessment: the 21–22 October 2019 extreme flood along the lower Orba River (Alessandria, NW Italy)

2021

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The knowledge of flood-induced ground effects and flood-water dynamics is a crucial issue for hydro-geomorphic hazards assessment and mapping, and thus for river management and land use planning. This paper and the related 1:15,000 map illustrate the lower Orba River (NW Italy) and its adjacent floodplain geomorphic response to the 21-22 October 2019 extreme flood. This was estimated to be a 500-years flood and caused severe damage to cultivated fields, structures, and infrastructures. The research is based on extensive post-flood field surveys, ante-and post-flood GNSS surveys, and aerial photographs interpretation. Intense inchannel sediment mobilization, bank retreats, and channelization structures collapses were surveyed. Furthermore, alluvial gullies, overbank deposits, crevasse splays, and surficialerosion evidences were mapped over the floodplain, along with the flooded area (17.65 km 2 ) and the flood-water features. A specific legend developed for flood-related and anthropogenic elements mapping in a typical lowland agricultural landscape with regulated rivers is proposed.

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Shallow landslides triggered by consecutive rainfall events at Catanzaro strait (Calabria–Southern Italy)

Cited by 32 publications

References 33 publications

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy

Flood-induced ground effects and flood-water dynamics for hydro-geomorphic hazard assessment: the 21–22 October 2019 extreme flood along the lower Orba River (Alessandria, NW Italy)

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