A new shallow landslides inventory for Southern Lunigiana (Tuscany, Italy) and analysis of predisposing factors

2018 ◽  
Vol 46 ◽  
pp. 149-154
Author(s):  
Enrico D'Addario ◽  
Emanuele Trefolini ◽  
Elisa Mammoliti ◽  
Michele Papasidero ◽  
Vincenzo Vacca ◽  
...  
Keyword(s):  
Shallow Landslides ◽  
Predisposing Factors

scholarly journals Integrating Satellite Soil Moisture and Rainfall Data on a Data-Driven Model for the Assessment of Shallow Landslides Hazard

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 42
Author(s):  
Meisina ◽  
Bordoni ◽  
Lucchelli ◽  
Brocca ◽  
Ciabatta ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Methodological Approach ◽  
Regional Scale ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Triggering Factors ◽  
Physically Based ◽  
Physically Based Models ◽  
Triggering Events

Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the events, according to the return time of the triggering events, which generally correspond to intense and concentrated rainfalls. Susceptibility and hazard of a territory are usually assessed by means of physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall amounts. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Moreover, physically-based models require, sometimes, significant computation power, which limit their implementations at regional scale. Data-driven models could overcome both of these limitations, even if they are generally built up taking into only the predisposing factors of shallow instabilities. Thus, they allow usually to estimate the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the hazard. This work presents the preliminary results of the development and the implementation of data-driven model able to estimate the hazard of a territory towards shallow landslides. The model is based on a Genetic Algorithm Model (GAM), which links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to the soil moisture content and to the rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in different catchments of 30–40 km2 located in Oltrepò Pavese area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Domenico Calcaterra ◽  
Marco Firpo ◽  
Giacomo Pepe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Meaningful Improvement ◽  
Susceptibility Evaluation ◽  
Landslide Runout

<p>Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.</p><p>Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).</p><p>In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.</p>

A data-drive model for the assessment of shallow landslides hazard with the integration of satellite soil moisture and rainfall data

2020 ◽  
Author(s):  
Valerio Vivaldi ◽  
Massimiliano Bordoni ◽  
Luca Lucchelli ◽  
Beatrice Corradini ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Methodological Approach ◽  
Return Time ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Probability Of Occurrence ◽  
Triggering Factors ◽  
Triggering Events ◽  
Spatio Temporal

<p>Rainfall-induced shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. For these reasons, it is necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the triggering events, according to the return time of them, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the susceptibility and hazard of a territory are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Data-driven models could constraints these, even if they are generally built up taking into only the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the development and the implementation of data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven technique. The model is based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in 30-40 km<sup>2</sup> wide catchments of Oltrepò Pavese hilly area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.</p>

Using satellite soil moisture and rainfall data for the monitoring and the prediction of natural hazards

2021 ◽  
Author(s):  
Valerio Vivaldi ◽  
Massimiliano Bordoni ◽  
Luca Brocca ◽  
Luca Ciabatta ◽  
Claudia Meisina
Keyword(s):  
Methodological Approach ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Probability Of Occurrence ◽  
Mechanical Responses ◽  
Triggering Factors ◽  
Physically Based ◽  
Adaptive Regression ◽  
Spatio Temporal

<p>Rainfall-induced shallow landslides affect buildings, roads, facilities, cultivations, causing several damages and, sometimes, loss of human lives. It is necessary assessing the most prone zones in a territory where these phenomena could occur and the triggering conditions of these events, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the spatial and temporal probability of occurrence are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, they are limited to be applied in a reliable way in little catchments, where geotechnical and hydrological characteristics of the materials are homogeneous. Data-driven models could constraints these, when the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, are combined with triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the implementation of a data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven techniques. The models are based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available thanks to satellite measures (ASCAT and GPM). The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apeninnes environment. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.</p>

scholarly journals Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 333
Author(s):  
Massimo Conforti ◽  
Fabio Ietto
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Susceptibility Map ◽  
Slope Gradient ◽  
Landslide Susceptibility Map ◽  
Jackknife Test ◽  
Susceptibility Model ◽  
Very High

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

Ill-being related to nursing: Study of predisposing factors

2010 ◽  
Author(s):  
Nuno Murcho ◽  
Saul de Jesus ◽  
Eusebio Pacheco ◽  
Andreia Pacheco
Keyword(s):  
Predisposing Factors

Effect of Isolated Medial Patellofemoral Ligament Reconstruction in Patellofemoral Instability Regardless of Predisposing Factors

2020 ◽  
Author(s):  
Jae Ik Lee ◽  
Mohd Shahrul Azuan Jaffar ◽  
Han Gyeol Choi ◽  
Tae Woo Kim ◽  
Yong Seuk Lee
Keyword(s):  
Medial Patellofemoral Ligament ◽  
Trochlear Dysplasia ◽  
Case Series ◽  
Patellofemoral Instability ◽  
International Knee Documentation Committee ◽  
Predisposing Factors ◽  
Mpfl Reconstruction ◽  
Patellar Tilt ◽  
Case Series Study

AbstractThe purpose of this study was to evaluate the outcomes of isolated medial patellofemoral ligament (MPFL) reconstruction, regardless of the presence of predisposing factors. A total of 21 knees that underwent isolated MPFL reconstruction from March 2014 to August 2017 were included in this retrospective series. Radiographs of the series of the knee at flexion angles of 20, 40, and 60 degrees were acquired. The patellar position was evaluated using the patellar tilt angle, sulcus angle, congruence angle (CA), and Caton-Deschamps and Blackburne-Peel ratios. To evaluate the clinical outcome, the preoperative and postoperative International Knee Documentation Committee (IKDC) and Lysholm knee scoring scales were analyzed. To evaluate the postoperative outcomes based on the predisposing factors, the results were separately analyzed for each group. Regarding radiologic outcomes, 20-degree CA was significantly reduced from 10.37 ± 5.96° preoperatively to −0.94 ± 4.11° postoperatively (p = 0.001). In addition, regardless of the predisposing factors, delta values of pre- and postoperation of 20-degree CA were not significantly different in both groups. The IKDC score improved from 53.71 (range: 18–74) preoperatively to 94.71 (range: 86–100) at the last follow-up (p = 0.004), and the Lysholm score improved from 54.28 (range: 10–81) preoperatively to 94.14 (range: 86–100) at the last follow-up (p = 0.010). Isolated MPFL reconstruction provides a safe and effective treatment for patellofemoral instability, even in the presence of mild predisposing factors, such as trochlear dysplasia, increased patella height, increased TT–TG distance, or valgus alignment. This is a Level 4, case series study.

Predisposing Factors, Demographics, Angiographic Features, and the Possible Role of Inflammation in Coronary Perforations—A 10-year Single-center Experience

2011 ◽  
Vol 9 (2) ◽  
pp. 87 ◽  
Author(s):  
Preeti Chandra ◽  
Saurav Chatterjee ◽  
Nishant Koradia ◽  
Deepak Thekkoott ◽  
Bilal Malik ◽  
...  
Keyword(s):  
Risk Factors ◽  
Artery Bypass ◽  
Bypass Graft ◽  
Coronary Intervention ◽  
Predisposing Factors ◽  
Covered Stents ◽  
Optimal Management ◽  
Balloon Inflation ◽  
Catheterization Laboratory ◽  
Cell Counts

Background:Coronary perforation during percutaneous coronary intervention is a rare but dreaded complication. The risk factors, optimal management, and outcome remain obscure.Objectives:To determine the predisposing factors, optimal management, and preventive strategies. We retrospectively looked at coronary perforations at our catheterization laboratory over the last 10 years. We reviewed patient charts and reports. Two independent operators, in a blinded approach, reviewed all procedural cineangiograms. Data were analyzed by simple statistical methodology.Results:Nine patients were treated conservatively and six patients were treated with prolonged balloon inflation. Six patients were treated with polytetrafluoroethylene (PTFE)-covered stents. One patient required emergency coronary artery bypass graft. No deaths were reported. Subjects with perforations also had a significantly higher total white blood cell count (means 12,134 versus 6,155, 95 % confidence interval [CI], p< 0.0001, n=22), total absolute neutrophil count (means 74.2 % versus 57.1 %, 95 % CI, p<0.0001, n=22), and neutrophil:lymphocyte ratio (means 3.65 versus 1.50, 95% CI, p<0.0001, n=22).Conclusions:Coronary perforations are rare but potentially fatal events. Hypertension, small vessel diameter, high balloon:artery ratio, use of hydrophilic wires, and presence of myocardial bridging appear to be possible risk factors. Most perforations can be treated conservatively or with prolonged balloon inflation using perfusion balloons. Use of PTFE-covered stents could be a life-saving measure in cases of large perforations. Subjects with perforations also had greater systemic inflammation as indicated by elevated white cell counts.

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