izpis_h1_title_alt

Izdelava kart nevarnosti pred snežnimi plazovi na primeru Zgornje Soške doline
ID Kostevc, Miha (Author), ID Kobal, Milan (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (8,60 MB)
MD5: 3AFC63CA6DECDC54EA93ABA2B9F172D4

Abstract
V sklopu magistrskega dela so bila z daljinsko vodenim letalnikom posneta območja odlaganja 11 snežnih plazov, ki so se 22. in 23. januarja 2021 sprožili v Zgornji Soški dolini. Na podlagi teh posnetkov so bili izdelani fotogrametrični oblaki točk, ortofoti (DOF) in digitalni modeli površja (DMP), iz katerih je bil za območja odlaganja določen doseg snežnih plazov in izračunana volumen ter višina plazovine. Volumen plazovine je znašal od 19.740 m3 (snežni plaz v Koritih) do 542.740 m3 (snežni plaz Komar). Maksimalna višina plazovine je znašala od 8,2 m (snežni plaz Peščenk) do 25,2 m (snežni plaz Komar). Glede na rezultate kalibracijskega modeliranja z modelom Flow-Py in skladnosti modelske napovedi z dejanskimi območji odlaganja sta bili določeni optimalni vrednosti vhodnih parametrov modela za dve skupini snežnih plazov. Pridobljeni sta bili s klastrsko analizo glede na vrednosti kota energijske črte (α), kjer so kazalniki prileganja napovedi dosegli maksimum. Z logistično regresijo sta bila ugotovljena dva dejavnika, ki ključno vplivata na razvrstitev v skupini, in sicer delež potencialnih območij proženja znotraj prispevnih območij in povprečni vzdolžni padec plaznice. Pri modeliranju v drugi fazi, ki je zajemalo vse plazove območja, sta bili uporabljeni optimalni vrednosti kota energijske črte α = 39,5⁰ za prvo in α = 29⁰ za drugo skupino. Rezultat modeliranja na podlagi optimizacije vhodnih parametrov je karta nevarnosti pred snežnimi plazovi na območju Zgornje Soške doline.

Language:Slovenian
Keywords:snežni plaz, Zgornja Soška dolina, Flow-Py, daljinsko vodeni letalnik
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:BF - Biotechnical Faculty
Place of publishing:Ljubljana
Publisher:[M. Kostevc]
Year:2022
PID:20.500.12556/RUL-140967 This link opens in a new window
UDC:630*5:630*4(497.4Zgornje Soška dolina)(043.2)=163.6
COBISS.SI-ID:122429443 This link opens in a new window
Publication date in RUL:22.09.2022
Views:940
Downloads:192
Metadata:XML DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Secondary language

Language:English
Title:Snow avalanche hazard mapping in the Upper Soča valley
Abstract:
In this research, the deposition zones of 11 snow avalanches that occurred on 22nd and 23rd of January 2021 in the Upper Soča Valley, were recorded using UAV. Point clouds, digital orthophoto (DOF) and digital surface model (DSM) were created based on these images, to determine the runout, volume (from 19,740 m3 at v Koritih avalanche to 542,741 m3 at Komar avalanche) and the height of the deposited debris (from 8.23 m at Peščenk avalanche to 25.22 m at Komar avalanche) in the runout zones. According to the results of calibration runs of the Flow-Py model (maximum values of the SI indicator from 0.810 at the Mesnovka avalanche to 0.983 at v Koritih avalanche), the optimal values of the input parameters for the two clusters of avalanches were determined. They were determined by cluster analysis according to the values of the energy line angle, where the goodness-of-fit indices reached a maximum. Two factors that have a key influence on the classification were identified with logistic regression, namely the proportion of potential release areas within the watersheds and the average longitudinal slope of the avalanche path. In the second phase modelling, which included all the avalanches, optimal values of the energy line angle α = 39.5⁰ for the first group and α = 29⁰ for the second group were used. The result of the second phase modelling based on the optimized input parameters is an avalanche hazard map of the Upper Soča Valley.

Keywords:snow avalanche, Upper Soča valley, Flow-Py, unmanned aerial vehicle

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back