Investigating landslide susceptibility procedures in Greece

Kavoura, Katerina; Sabatakakis, N.

doi:10.1007/s10346-019-01271-y

Cited by 42 publications

(21 citation statements)

References 105 publications

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“…In this field, many researchers have used different models to assess the susceptibility of geohazards on different scales according to different factors, such as an artificial neural network model [33], a bivariate statistical approach [34], a Bayesian model [35], a machine learning method [36] and an information value model (IVM) [37,38]. For large-scale railway design problems, we adopt the IVM because it can assess the geo-susceptibility of a broad area very quickly.…”

Section: Geological Danger Of F-regionsmentioning

confidence: 99%

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Xie

Schonfeld

et al. 2021

Sustainability

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Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation.

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Section: Geological Danger Of F-regionsmentioning

confidence: 99%

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Xie

Schonfeld

et al. 2021

Sustainability

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“…Whereas, quantitative methods can be classified as deterministic and probabilistic methods which involve the mathematical relationship between landslides' occurrences and their associated affecting factors. Statistical and Machine Learning methods such as Binary Logistic Regression, Information Value, Likelihood Ratio, Logistic Regression (LR), and Multivariate Regression are some the quantitative methods applied for landslide susceptibility modeling and mapping [12][13][14][15]. Furthermore, few other algorithms with optimum performance and a higher degree of accuracy used in the landslide susceptibility mapping include Discriminant Analysis, Generalized Additive Models, Evidential Belief Functions, Weighted Linear Combinations, and Weights of Evidence [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Tran

Dam

Jalal

et al. 2021

Mathematical Problems in Engineering

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The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

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“…Moreover, the simulation before the landslide runout is more critical of managing a future risk as a result of debris flow. Inundation zones based on the best-and worst-case scenarios depending on the mobility of failed materials and basal geometry and thickness of the landslide can be predicted by numerical models [54][55][56]. The outcomes from SAR and InSAR can help derive landslide thickness as crucial inputs for such runout models [57].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Studying Hydrology-Driven Landslide Hazards in Northwestern US Using Satellite InSAR, Precipitation and Soil Moisture Observations: Early Results and Future Directions

Lü

Kim

2021

GeoHazards

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The mountainous Pacific Northwest is prone to heavy winter rainfall, resulting in hundreds of landslides per year, human casualties, and billions of dollars of property damage. Precipitation is a major hydrologic trigger for landslides in the northwestern US and around the world. This paper reviews existing literature to outline a framework to study the linkage between precipitation and landslide hazards over the northwestern US using satellite remote sensing techniques including interferometric synthetic aperture radar (InSAR) methods, Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products, and Soil Moisture Active Passive (SMAP) satellite soil moisture data, along with correlation analysis and numerical modeling. InSAR time-series displacements provide an indication of landslide occurrence and extent, and help characterize the basal slip surface and slide-body volume based on the law of mass conservation. Precipitation and soil moisture sensed from the space and ground contribute to creating hydrogeological models associated with water infiltration. These crucial parameters are tracked through correlation and slope stability analysis to understand landslide dynamics. We highlight the results on mapping landslides over the state of Washington and analyses at a few select sites over southern Washington and southwestern Oregon. We conclude that satellite observations of landslide motions and the attributing hydrological variables from both radar and optical images improve our understanding of the inter-relationships between the hydrologic processes along with topographic and geologic settings, and the landslide kinematics and mechanisms inferred from time-series measurements and landslide modeling on a regional scale.

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Investigating landslide susceptibility procedures in Greece

Cited by 42 publications

References 105 publications

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

A Framework for Studying Hydrology-Driven Landslide Hazards in Northwestern US Using Satellite InSAR, Precipitation and Soil Moisture Observations: Early Results and Future Directions

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