Projects / Programmes
Deep-seated landslide prediction modelling based on a combination of physical modelling and a data-driven approach
| Code |
Science |
Field |
Subfield |
| 1.06.06 |
Natural sciences and mathematics |
Geology |
Regional geology |
| Code |
Science |
Field |
| 1.05 |
Natural Sciences |
Earth and related Environmental sciences |
landslide, prediction, triggering mechanisms, real-time monitoring, physical model, data-driven model
Researchers (1)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
35426 |
PhD Tina Peternel |
Geology |
Head |
2020 - 2022 |
156 |
Organisations (1)
| no. |
Code |
Research organisation |
City |
Registration number |
No. of publicationsNo. of publications |
| 1. |
0215 |
Geological Survey of Slovenia |
Ljubljana |
5051410000 |
11,243 |
Abstract
Deep-seated rotational landslides are a common and widespread phenomenon in Slovenia, as they are in most European countries. This type of slope mass movement is largely related to saturated soils of low permeability, such as clays or silts. Because we cannot avoid the risk of landslides and must live with it, it is important to understand and predict landslide dynamics. Research on landslide dynamics forms the basis of landslide hazard prevention and also serves as a basic requirement for the development of prediction models and for defining prevention and mitigation measures. The principal aim of the proposed project is to develop models to predict real-time dynamics of deep-seated landslides using quantitative methods such as physical modelling and data-driven approaches. The methodology of the proposed project is based on monitoring and recognition of landslide triggering mechanisms and their interactions. In order to determine the dynamics of landslides the following key parameters will be studied: engineering-geological conditions (EG units; landslide features, results from core logging); geotechnical near-real time monitoring data (absolute displacements obtained from inclinometers); hydro-meteorological real-time monitoring data (groundwater levels, precipitation), geodetic monitoring data (surface movement patterns) and real-time displacement data obtained from real-time monitoring system. Project will be achieved through the implementation of the following activities: state of-the-art methods and technologies on landslide monitoring and prediction; characterization of intrinsic (as geological conditions) and extrinsic factors (such as hydro-meteorological conditions) that lead to the initiation of deep-seated landslides; intercorrelation of landslide triggering mechanisms using data processing of existing engineering geological (EG units; results from core-logging), geotechnical (magnitude, depth, direction of ground movement) and hydro-meteorological data (pore water pressure, ground water levels, precipitation); determination of landslide kinematics using near-real-time monitoring data (physical-based model); comparison of applicability of monitoring techniques (geotechnical sensors) used to monitor displacements; relationship between displacements and landslide triggering mechanisms using time-series analysis of real-time monitoring data (displacement velocity, ground water level and precipitation); development of an algorithm to predict landslide based on a data-driven approach and physical-based model; portability and applicability of the developed approach to other areas prone to deep-seated landslides. Project activities will be implemented in the area above the settlement of Koroška Bela (NW Slovenia, Karavanke) which exhibits a number of deep-seated landslides (the Urbas and Čikla landslides) in weathered siltstone and claystone. These landslides pose a direct threat to the village below (with approximately 2,200 inhabitants). Despite the fact that the methodology developed to predict deep-seated landslide dynamics will be developed for a specific pilot area, the methodology will also be tested in other areas that are subject to deep-seated landslides. This will make an important contribution to reducing the hazards associated with landslides.
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