Iskanje po katalogu digitalne knjižnice

Opis: Mountain resilience is the ability of mountain regions to endure, adapt to, and recover from environmental, climatic, and anthropogenic stressors. Due to their steep topography, extreme weather conditions, and unique biodiversity, these areas are particularly vulnerable to climate change, natural hazards, and human activities. This paper examines how nature-based solutions (NbSs) can strengthen slope stability and geotechnical resilience, with a specific focus on Slovenia’s sub-Alpine regions as a case study representative of the Alps and similar mountain landscapes worldwide. The proposed Climate-Adaptive Resilience Evaluation (CARE) concept integrates geomechanical analysis with geotechnical planning, addressing the impacts of climate change through a systematic causal chain that connects climate hazards, their effects, and resulting consequences. Key factors such as water infiltration, soil permeability, and groundwater dynamics are identified as critical elements in designing timely and effective NbSs. In scenarios where natural solutions alone may be insufficient, hybrid solutions (HbSs) that combine nature-based and conventional engineering methods are highlighted as essential for managing unstable slopes and restoring collapsed geostructures. The paper provides practical examples, including slope stability analyses and reforestation initiatives, to illustrate how to use the CARE concept and how NbSs can mitigate geotechnical risks and promote sustainability. By aligning these approaches with regulatory frameworks and climate adaptation objectives, it underscores the potential for integrating NbSs and HbSs into comprehensive, long-term geotechnical strategies for enhancing mountain resilience.
Ključne besede: mountain resilience, climate change, nature-based solutions (NbSs), hybrid solutions (HbSs), geotechnical planning, slope stability, landslides
Objavljeno v DKUM: 31.03.2025; Ogledov: 0; Prenosov: 9
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Opis: In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starča Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms.
Ključne besede: landslides, support vector machines, decision trees classifier, Starča Basin
Objavljeno v DKUM: 13.06.2018; Ogledov: 1268; Prenosov: 70
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