scholarly journals Shallow Landslides Physically Based Susceptibility Assessment Improvement Using InSAR. Case Study: Carpathian and Subcarpathian Prahova Valley, Romania

2021 ◽  
Vol 13 (12) ◽  
pp. 2385
Author(s):  
Iuliana Armaș ◽  
Mihaela Gheorghe ◽  
George Cătălin Silvaș
Keyword(s):  
Slope Failure ◽  
Surface Displacement ◽  
Unit Weight ◽  
Susceptibility Map ◽  
Case Scenario ◽  
Worst Case ◽  
Infinite Slope ◽  
Actual Surface ◽  
Infinite Slope Stability Model ◽  
Physically Based

A multi-temporal satellite radar interferometry technique is used for deriving the actual surface displacement patterns in a slope environment in Romania, in order to validate and improve a landslide susceptibility map. The probability the occurrence of future events is established using a deterministic approach based on a classical one-dimension infinite slope stability model. The most important geotechnical parameters for slope failure in the proposed study area are cohesion, unit weight and friction angle, and the triggering factor is a rapid rise in groundwater table under wetting conditions. Employing a susceptibility analysis using the physically based model under completely saturated conditions proved to be the most suitable scenario for identifying unstable areas. The kinematic characteristics are assessed by the Small BAseline Subsets (SBAS) interferometry technique applied to C-band synthetic aperture radar (SAR) Sentinel-1 imagery. The analysis was carried out mainly for inhabited areas which present a better backscatter return. The validation revealed that more than 22% of the active landslides identified by InSAR were predicted as unstable areas by the infinite slope model. We propose a refinement of the susceptibility map using the InSAR results for unravelling the danger of the worst-case scenario.

scholarly journals Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy

Geosciences ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 309
Author(s):  
Federico Valerio Moresi ◽  
Mauro Maesano ◽  
Alessio Collalti ◽  
Roy C. Sidle ◽  
Giorgio Matteucci ◽  
...  
Keyword(s):  
Forest Cover ◽  
Shallow Landslides ◽  
Susceptibility Map ◽  
Infinite Slope ◽  
South Italy ◽  
Physically Based ◽  
Root Strength ◽  
Forested Areas ◽  
Forest Mountain

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards.

scholarly journals Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

2019 ◽  
Author(s):  
Johnnatan Palacio Cordoba ◽  
Martin Mergili ◽  
Edier Aristizábal
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Confusion Matrix ◽  
Hazard Mapping ◽  
Infinite Slope ◽  
Susceptibility Analysis ◽  
Colombian Andes ◽  
Infinite Slope Stability Model ◽  
Stability Model ◽  
Physically Based

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and Receiver Operating Characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.

scholarly journals Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

2017 ◽  
Vol 17 (7) ◽  
pp. 1091-1109 ◽  
Author(s):  
Sérgio C. Oliveira ◽  
José L. Zêzere ◽  
Sara Lajas ◽  
Raquel Melo
Keyword(s):  
Landslide Susceptibility ◽  
Test Site ◽  
Information Value ◽  
Susceptibility Map ◽  
Predictive Capacity ◽  
Infinite Slope ◽  
Susceptibility Maps ◽  
Basin Scale ◽  
Deterministic Methods ◽  
Physically Based

Abstract. Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

scholarly journals Combination of empirically-based and physically-based methods to assess shallow slides susceptibility at the basin scale

2016 ◽  
Author(s):  
Sérgio C. Oliveira ◽  
José L. Zêzere ◽  
Sara Lajas ◽  
Raquel Melo
Keyword(s):  
Landslide Susceptibility ◽  
Test Site ◽  
Information Value ◽  
Susceptibility Map ◽  
Predictive Capacity ◽  
Infinite Slope ◽  
Susceptibility Maps ◽  
Basin Scale ◽  
Deterministic Methods ◽  
Physically Based

Abstract. Approaches used to assess shallow slides susceptibility at the basin scale are conceptually different depending on the use of empirically-based or physically-based methods. The former are sustained by the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the opposing forces that promote resistance to movement. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodological distinct, the statistic and deterministic methods generate similar shallow slides susceptibility results regarding the model’s predictive capacity and spatial agreement; and (ii) the combination of shallow slides susceptibility maps obtained with empirically-based and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow slides occurrence. These hypotheses were tested in a small test site (13.9 km2) located north of Lisbon (Portugal), using a empirically-based method (the Information Value method) and a physically-based method (the Infinite Slope method). The landslide susceptibility maps produced with the statistic and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross-tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slides susceptibility map, which combines the independent results obtained with empirically-based and physically-based models. Moreover the combination of the two models allowed the identification of areas where the results of the Information Value and the Infinite Slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

scholarly journals Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

2020 ◽  
Vol 20 (3) ◽  
pp. 815-829 ◽  
Author(s):  
Johnnatan Palacio Cordoba ◽  
Martin Mergili ◽  
Edier Aristizábal
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Confusion Matrix ◽  
Hazard Mapping ◽  
Infinite Slope ◽  
Susceptibility Analysis ◽  
Colombian Andes ◽  
Infinite Slope Stability Model ◽  
Stability Model ◽  
Physically Based

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.

scholarly journals Rainfall-induced shallow landslides and soil wetness: comparison of physically-based and probabilistic predictions

2020 ◽  
Author(s):  
Elena Leonarduzzi ◽  
Brian W. McArdell ◽  
Peter Molnar
Keyword(s):  
Hydrological Model ◽  
Soil Depth ◽  
Probabilistic Approach ◽  
Soil Parameters ◽  
Infinite Slope ◽  
Coarse Resolution ◽  
Infinite Slope Stability Model ◽  
Physically Based ◽  
Soil Wetness ◽  
Parameter Constraints

Abstract. Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically-based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only ~20 % of the area with typical soil parameters and soil depth models. Second, the physically-based approach with a coarse resolution model setup (TerrSysMP) 12.5 km × 12.5 km downscaled to 25 m × 25 m with the TopographicWetness Index to provide water table simulations for the infinite slope stability model did not succeed in predicting local scale landsliding satisfactorily, despite spatial downscaling. We argue that this is due to inadequacies of the infinite slope model, soil parameter uncertainty, and the coarse resolution of the hydrological model. Third, soil saturation estimates provided by a higher resolution 500 m × 500 m conceptual hydrological model (PREVAH) provided added value to rainfall threshold curves for landslide prediction in the probabilistic approach, with potential to reduce false alarms and misses. We conclude that although combined physically-based hydrological-geotechnical modelling is the desired goal, we still need to overcome problems of model resolution, parameter constraints, and landslide validation for successful prediction at regional scales.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

EXPEDITION TO MALI, 2020

2020 ◽  
pp. 12-28
Author(s):  
D. V. Vaniukova ◽  
◽  
P. A. Kutsenkov ◽  
Keyword(s):  
Mass Murder ◽  
The State ◽  
Case Scenario ◽  
Worst Case ◽  
Traditional Art ◽  
Peter The Great ◽  
State Museum ◽  
Bobo Dioulasso ◽  
Oriental Studies ◽  
The Village

The research expedition of the Institute of Oriental studies of the Russian Academy of Sciences has been working in Mali since 2015. Since 2017, it has been attended by employees of the State Museum of the East. The task of the expedition is to study the transformation of traditional Dogon culture in the context of globalization, as well as to collect ethnographic information (life, customs, features of the traditional social and political structure); to collect oral historical legends; to study the history, existence, and transformation of artistic tradition in the villages of the Dogon Country in modern conditions; collecting items of Ethnography and art to add to the collection of the African collection of the. Peter the Great Museum (Kunstkamera, Saint Petersburg) and the State Museum of Oriental Arts (Moscow). The plan of the expedition in January 2020 included additional items, namely, the study of the functioning of the antique market in Mali (the “path” of things from villages to cities, which is important for attributing works of traditional art). The geography of our research was significantly expanded to the regions of Sikasso and Koulikoro in Mali, as well as to the city of Bobo-Dioulasso and its surroundings in Burkina Faso, which is related to the study of migrations to the Bandiagara Highlands. In addition, the plan of the expedition included organization of a photo exhibition in the Museum of the village of Endé and some educational projects. Unfortunately, after the mass murder in March 2019 in the village of Ogossogou-Pel, where more than one hundred and seventy people were killed, events in the Dogon Country began to develop in the worst-case scenario: The incessant provocations after that revived the old feud between the Pel (Fulbe) pastoralists and the Dogon farmers. So far, this hostility and mutual distrust has not yet developed into a full-scale ethnic conflict, but, unfortunately, such a development now seems quite likely.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

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