scholarly journals Heavy Rainfall Triggering Shallow Landslides: A Susceptibility Assessment by a GIS-Approach in a Ligurian Apennine Catchment (Italy)

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 605 ◽  
Author(s):  
Anna Roccati ◽  
Francesco Faccini ◽  
Fabio Luino ◽  
Andrea Ciampalini ◽  
Laura Turconi
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslides ◽  
Decision Makers ◽  
Slope Instability ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Susceptibility Assessment ◽  
Rainfall Events ◽  
Earth Flows ◽  
Weighted Values

In recent decades, the Entella River basin (eastern Liguria) has been affected by several rainfall events that induced widespread shallow landslides and earth flows on the slopes; roads, buildings, structures and infrastructure suffered extensive damage due to the instability processes. In this paper, a GIS-based approach for analyzing and assessing a simplified landslide susceptibility in the Entella River catchment is presented. Starting from landslide information mainly provided from newspaper articles and unpublished reports from municipal archives, we performed a series of comparative analyses using a set of thematic maps to assess the influence of predisposing natural and anthropic factors. By evaluating the statistical distribution of landslides in different categories, we assigned weighted values to each parameter, according to their influence on the instability processes. A simplified, reproducible, but effective approach to assess landslide susceptibility in the study area was performed by combining all predisposing factors. The resulting scores in proneness to slope instability classes may be used to generate a simplified landslides susceptibility map of the catchment area which would be easy to regularly update every time a rainfall event that is able to trigger shallow landslides occurs; this would provide a useful tool for local authorities and decision makers for identifying areas which could potentially be affected by instability processes, and would help in determining the most suitable measures in land-planning and landslide risk management.

scholarly journals Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Giuseppe Bausilio ◽  
Domenico Calcaterra ◽  
Pierluigi Confuorto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Geographical Information ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Learning Techniques

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

scholarly journals Rainfall events with shallow landslides in the Entella catchment, Liguria, northern Italy

2018 ◽  
Vol 18 (9) ◽  
pp. 2367-2386 ◽  
Author(s):  
Anna Roccati ◽  
Francesco Faccini ◽  
Fabio Luino ◽  
Laura Turconi ◽  
Fausto Guzzetti
Keyword(s):  
River Basin ◽  
Rainfall Intensity ◽  
Geographical Location ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Shallow Landslides ◽  
Landslide Risk ◽  
Antecedent Rainfall ◽  
Rainfall Events ◽  
Earth Flows

Abstract. In recent decades, the Entella River basin, in the Liguria Apennines, northern Italy, was hit by numerous intense rainfall events that triggered shallow landslides and earth flows, causing casualties and extensive damage. We analyzed landslide information obtained from different sources and rainfall data recorded in the period 2002–2016 by rain gauges scattered throughout the catchment, to identify the event rainfall duration, D (in h), and rainfall intensity, I (in mm h−1), that presumably caused the landslide events. Rainfall-induced landslides affected the whole catchment area, but were most frequent and abundant in the central part, where the three most severe events hit on 23–24 November 2002, 21–22 October 2013 and 10–11 November 2014. Examining the timing and location of the slope failures, we found that the rainfall-induced landslides occurred primarily at the same time or within 6 h from the maximum peak rainfall intensity, and at or near the geographical location where the rainfall intensity was largest. Failures involved mainly forested and natural surfaces, and secondarily cultivated and terraced slopes, with different levels of maintenance. Man-made structures frequently characterize the landslide source areas. Adopting a frequentist approach, we define the event rainfall intensity–event duration (ID) threshold for the possible initiation of shallow landslides and hyper-concentrated flows in the Entella River basin. The threshold is lower than most of the curves proposed in the literature for similar mountain catchments, local areas and single regions in Italy. The result suggests a high susceptibility to rainfall-induced shallow landslides of the Entella catchment due to its high-relief topography, geological and geomorphological settings, meteorological and rainfall conditions, and human interference. Analysis of the antecedent rainfall conditions for different periods, from 3 to 15 days, revealed that the antecedent rainfall did not play a significant role in the initiation of landslides in the Entella catchment. We expect that our findings will be useful in regional to local landslides early warning systems, and for land planning aimed at reducing landslide risk in the study area.

scholarly journals InSAR Monitoring of Landslide Activity in Dominica

2021 ◽  
Vol 13 (4) ◽  
pp. 815
Author(s):  
Mary-Anne Fobert ◽  
Vern Singhroy ◽  
John G. Spray
Keyword(s):  
Risk Mitigation ◽  
Disaster Mitigation ◽  
Aerial Images ◽  
Landslide Inventory ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Interferometric Synthetic Aperture Radar ◽  
Rainfall Events ◽  
Susceptibility Maps ◽  
Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

scholarly journals From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

2021 ◽  
Author(s):  
Luguang Luo ◽  
Luigi Lombardo ◽  
Cees van Westen ◽  
Xiangjun Pei ◽  
Runqiu Huang
Keyword(s):  
Ground Motion ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Additive Model ◽  
Slope Instability ◽  
Susceptibility Map ◽  
Jiuzhaigou Earthquake ◽  
Full Spectrum ◽  
The Past ◽  
Time Invariant

AbstractThe vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ($$M_w = 6.5$$ M w = 6.5 ) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

scholarly journals Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal)

2004 ◽  
Vol 4 (1) ◽  
pp. 133-146 ◽  
Author(s):  
J. L. Zêzere ◽  
E. Reis ◽  
R. Garcia ◽  
S. Oliveira ◽  
M. L. Rodrigues ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Slope Instability ◽  
Rate Curve ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Return Periods ◽  
Data Set ◽  
Prediction Rate ◽  
Definition Of

Abstract. A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal) where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use). Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model) over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i) the definition of thresholds of rainfall (quantity-duration) responsible for past landslide events; (ii) the calculation of the relevant return periods; (iii) the assumption that the same rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area). The landslide hazard is present as the probability of each pixel to be affected by a slope movement, and results from the coupling between the susceptibility map, the prediction-rate curve, and the return periods of critical rainfall events, on a scenario basis. Using this methodology, different hazard scenarios were assessed, corresponding to different rain paths with different return periods.

scholarly journals LANDSLIDE SUSCEPTIBILITY MODELLING IN SELECTED STATES ACROSS SE. NIGERIA

2020 ◽  
Vol 4 (1) ◽  
pp. 23-27
Author(s):  
R. O. E. Ulakpa ◽  
V.U.D. Okwu ◽  
K. E. Chukwu ◽  
M. O. Eyankware
Keyword(s):  
Landslide Susceptibility ◽  
Landsat Imagery ◽  
Control Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Landsat 8 ◽  
Aerial Photos ◽  
Anambra State ◽  
Digital Elevation ◽  
Elevation Model

Identification and mapping of landslide is essential for landslide risk and hazard assessment. This paper gives information on the uses of landsat imagery for mapping landslide areas ranging in size from safe area to highly prone areas. Landslide mitigation largely depends on the understanding of the nature of the factors namely: slope, soil type, lineament, lineament density, elevation, rainfall and vegetation. These factors have direct bearing on the occurrence of landslide. Identification of these factors is of paramount importance in setting out appropriate and strategic landslides control measures. Images for this study was downloaded by using remote sensing with landsat 8 ETM and aerial photos using ArcGIS 10.7 and Surfer 8 software, while Digital Elevation Model (DEM) and Google EarthPro TM were used to produce slope, drainage, lineament and elevation. From the processed landsat 8 imagery, landslide susceptibility map was produced, and landslide was category into various class; low, medium and high. From the study, it was observed that Enugu and Anambra state ranges from high to medium in terms of landslide susceptibility, Imo state ranges from medium to low.

scholarly journals Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors

2011 ◽  
Vol 11 (5) ◽  
pp. 1475-1497 ◽  
Author(s):  
F. Vergari ◽  
M. Della Seta ◽  
M. Del Monte ◽  
P. Fredi ◽  
E. Lupia Palmieri
Keyword(s):  
Landslide Susceptibility ◽  
Agricultural Practices ◽  
Selection Procedure ◽  
Assessment Method ◽  
Decision Makers ◽  
Susceptibility Assessment ◽  
Multi Temporal ◽  
Validation Procedure ◽  
Potential Factors ◽  
Factor Maps

Abstract. In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy), where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor maps. Finally, a validation procedure was applied based on the partition of the landslide inventory from multi-temporal aerial photo interpretation. Although encompassing some sources of uncertainties, the applied susceptibility assessment method provided a satisfactory and unbiased prediction for the Upper Orcia Valley. The results confirmed the efficiency of the selection procedure, as an unbiased step of the landslide susceptibility evaluation. Furthermore, we achieved the purpose of presenting a conceptually simple but, at the same time, effective statistical procedure for susceptibility analysis to be used as well by decision makers in land management.

scholarly journals LANDSLIDE SUSCEPTIBILITY MAPPING BY GIS-BASED QUALITATIVE WEIGHTING PROCEDURE IN CORINTH AREA

2018 ◽  
Vol 36 (2) ◽  
pp. 904 ◽  
Author(s):  
M. Foumelis ◽  
E. Lekkas ◽  
I. Parcharidis
Keyword(s):  
Landslide Susceptibility ◽  
Rapid Assessment ◽  
Time Frame ◽  
Susceptibility Mapping ◽  
Slope Instability ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Potential Index ◽  
Degree Of Stability ◽  
Data Layers

Landslide susceptibility mapping refers to a division of the land into zones of varying degree of stability based on an estimated significance of causative factors in inducing the instability. Maps of landslide susceptibility (relative hazard) are usually prepared on regional scales from 1:25.000 - 1:50.000. An advantage of regional studies is that they allow rapid assessment and hence larger areas can be covered in short durations. Factors (data layers) used for the preparation of the landslide susceptibility map were obtained from different sources such as topographic maps, geological maps and satellite images. All the above data layers were converted to raster format in the GIS, each representing an independent variable of a constructed spatial database. Computerization of the database would be necessary to make such analysis possible within an acceptable time frame. According to their relative importance to slope instability in the study area, the various classes of different data layers were assigned weights between 0,0 and 1,0 (collectively adding to 1,0). The overall susceptibility was calculated as an index named SPI (Susceptibility Potential Index), expressing the combination of the different weighted layers into a single map using a certain combination rule. Reclassification of susceptibility scores, based on natural breaks in the cumulative frequency histogram of SPI values, were used to delineate various susceptibility zones namely, very high, high, moderate, low and very low. Verification of results by overlaying susceptibility map and landslide inventory data and adjustment of zone's boundaries was the last stage of the study, allowing the reconsideration in some cases of the weights given

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