Projects / Programmes
Mathematical models for predicting landslide prone areas
| Code |
Science |
Field |
Subfield |
| 1.06.00 |
Natural sciences and mathematics |
Geology |
|
| Code |
Science |
Field |
| P510 |
Natural sciences and mathematics |
Physical geography, geomorphology, pedology, cartography, climatology |
landslides, prediction, mathematical models, spatial analiysis
Researchers (2)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
18166 |
PhD Marko Komac |
Geology |
Researcher |
2004 - 2006 |
521 |
| 2. |
01404 |
PhD Bojan Ogorelec |
Geology |
Head |
2004 - 2006 |
369 |
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
Past research in the field of the landslide prone areas prediction in Slovenia (Petkovšek et al., 1993; Ribičič et al., 1994; Ribičič & Šinigoj, 1996; Vukadin & Ribičič, 1998; Urbanc et al., 2000; Komac 2003; Komac, v tisku) and in the rest of the world (Neuland, 1976; DeGraff & Romesburg, 1984; Pack, 1985; Bernkopf, 1988; Pike, 1988; Corominas, 1992; Othman et al., 1992; Atkinson & Massari, 1996; Chung & Fabbri, 1999; Halounova, 1999; Sinha et al., 1999; Syarief et al., 1999; Chung & Shaw, 2000; Gorsevski et al., 2000a; Gorsevski et al., 2000b; Dhakal et al., 2000; Vestal, 2002) have shown that it is possible to predict landslide prone areas (and other hillslope mass movements) with an acceptable reliability using methods, based on the subjective expert decisions, or even better, based on objective statistical prediction models. Numerous data on spatial factors that potentially influence the landslide occurrence, and numerous methodologies, automatically pose the question which method or model is better, why it is so, and what are its deficiencies. We will focus the research into better understanding of the relation between spatial factors and landslide occurrence, and into better understanding of the correlation between useful spatial factors. Analyses will enable us to determine the applicability of new, merged images, in more detailed detection of hillslope mass movements, such as landslides, debris flows, slides, etc. Positive results will enable a better use of these data in the engineering geology, in the quartar geology, in the sedimenthology, and in the structural geology. Given results will be upgraded with their incorporation into the process of the landslide prediction models development. At the same time we will pursuit the search for the best possible mathematical model for predicting landslide prone areas. Beside the geological use, there are several other possibilities of the applicability of the results, ieg. forestry, agriculture, insurance, and spatial planning.
