In geosciences modelling is rather quickly developing discipline. Statistical modelling of landslide susceptibility is relatively more traditional approach. Nevertheless, more complicated statistical methods are being developed and applied on larger areas. This development is caused especially by increasing computational capacity and software. This diploma thesis summarises existing statistical landslide susceptibility modelling approaches. In the following part, several landslide susceptibility models were created for the area of Czechia. These models were created using logistic regression, naive Bayes and artificial neural network (ANN). Additionally, two more models were created using expert driven approach. All models were made using thirteen conditioning factors, i.e.elevation, slope, engineering geological regions, climatic areas, mean annual precipitation, topographic wetness index (TWI), aspect, orogenetic class, distance from confirmed fault, distance from watercourse, internal relief, land cover and slope shape. Models driven by statistical approach were created using Orange software. Landslide inventories that were used for construction of all models are based on two databases: "Registr svahových nestabilit" and "Registr sesuvů-Geofond". Using validation by SRC, PRC and ROC curves...
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:388507 |
| Date | January 2018 |
| Creators | Racek, Ondřej |
| Contributors | Blahůt, Jan, Klimeš, Jan |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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