magistrsko delo
Romi Koželj (Author), Lovro Šubelj (Mentor), Jure Bajc (Co-mentor)

Abstract

Potres je naravni pojav, ki nastane kot posledica notranje Zemljine dinamike globoko pod površjem in ga z današnjim znanjem še ni mogoče napovedati. V magistrskem delu poizkušamo z vidika omrežne znanosti pridobiti nova znanja o značilnostih in razvoju seizmične aktivnosti skozi čas. Implementiramo in med seboj primerjamo različne modele omrežij, ki temeljijo na interakciji med potresi v času in kraju ter na predpostavki o podobnostih potresne aktivnosti na izbranem geografskem območju. Iz omrežij, ki jih konstruiramo v več manjših zaporednih časovnih oknih, izračunamo nabor značilk ter prikažemo njihovo spreminjanje skozi čas. Na koncu z uporabo modela ARIMA za napovedovanje časovnih vrst preverimo, ali je iz dobljenih vzorcev moč sklepati o značilnostih seizmičnega dogajanja v prihodnosti. Analiza dobljenih omrežij ter generiranih časovnih vrst pokaže, da z večino obravnavanih modelov omrežij konstruiramo smiselna omrežja, preko katerih dobimo zanesljiv in predvidljiv odziv vrednosti v časovnih vrstah. Iz rezultatov napovedovanja vrednosti časovnih vrst je razvidno, da oblike časovnih vrst, predvsem v intervalih, v katerih se zgodi močnejši potres, z uporabljenim modelom ARIMA ne moremo dobro napovedati.

Keywords

analiza omrežij;seizmična aktivnost;časovne vrste;magisteriji;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [R. Koželj]
UDC: 004.414.23:550.34(043.2)
COBISS: 125574403 Link will open in a new window
Views: 26
Downloads: 7
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Other data

Secondary language: English
Secondary title: Modeling the time evolution of seismic activity using network analysis
Secondary abstract: An earthquake is a natural phenomenon that occurs as a consequence of Earth's internal dynamics. It originates deep under the planet's surface and cannot be predicted with our current knowledge. Thesis addresses a network analysis approach to acquire new knowledge about the characteristics and development of seismic activity over time. We implement and compare various network models based on time and space interactions between earthquakes and on the assumption of self-similarity in seismic activity in a selected geographic area. Using networks that are generated in multiple consecutive time windows, we extract a feature set and present its changes over time in a time series. Finally, ARIMA model for time series prediction is used to verify if it is possible to predict characteristics of seismic activity in the future. Analysis of generated networks and time series shows that the majority of used network models produce relevant networks that return reliable and predictable response in a time series. However, using ARIMA model to predict new data points in a time series turns out to be insufficient, especially in time intervals when the strong earthquake occurs.
Secondary keywords: network analysis;seismic activity;earhtquakes;time series;computer science;computer and information science;master's degree;Modeliranje podatkov (računalništvo);Potresi;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Master's thesis/paper
Study programme: 1000471
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 123 str.
ID: 16619327