Određivanje poplavljenog područja na području Karlovačke i Sisačko-moslavačke županije korištenjem multispektralnih satelitskih snimaka Sentinel-2 u programskom jeziku R

master's thesis

• Title: Određivanje poplavljenog područja na području Karlovačke i Sisačko-moslavačke županije korištenjem multispektralnih satelitskih snimaka Sentinel-2 u programskom jeziku R : diplomski rad
• Title (english): Determination of the flooded area in the area of Karlovačka and Sisačko-moslavacka counties using multispectral satellite images Sentinel-2 in software language R : master's thesis
• Author: Iva Drešar
• Mentor: Ivan Medved (mentor)
• Committee member: Ivan Medved (predsjednik povjerenstva)
• Committee member: Jasna Orešković (član povjerenstva)
• Committee member: Tomislav Korman (član povjerenstva)
• Granter: University of Zagreb; Faculty of Mining, Geology and Petroleum Engineering; (Department of Geophysical Exploration and Mine Surveying); Zagreb
• Defense date and country: 2024-09-18, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mining, Petroleum and Geology Engineering; Geological Engineering

Abstract

Klasifikacija terena poplavljenog područja Karlovačke i Sisačko-moslavačke županije korištenjem multispektralnih satelitskih snimaka Sentinel-2 prije i poslije poplave. Satelitske snimke preuzete sa službene stranice geoportala Copernicus. Osim satelitskih snimaka, ulazni podaci su područje istraživanja i poligoni za treniranje izrađeni u programu ArcMap. Klasifikacija terena izvedena je strojnim učenjem korištenjem algoritama Random Forest, Support Vector Machine i Neural Network u programskom jeziku R.

Abstract (english)

Classification of flood-affected areas in the Karlovac and Sisak-Moslavina counties using Sentinel-2 multispectral satellite images before and after the flood. The satellite images were downloaded from the official Copernicus geoportal website. In addition to the satellite images, the input data include the study area and training polygons created in ArcMap. The terrain classification was performed using machine learning algorithms, including Random Forest, Support Vector Machine, and Neural Network, in the R programming language.

• Keywords: poplavljena područja; klasifikacija; predikcija; Sentinel-2; RF; SVM; NNET; ArcMap; R; NDWI
• Keywords (english): flooded areas; classification; prediction; Sentinel-2; RF; SVM; NNET; ArcMap; R; NDWI
• Language: croatian
• URN:NBN: urn:nbn:hr:169:523953
• Study programme: Title: Geological engineering; specializations in: Hydrogeology and engineering geology, Environmental geology; Course: Hydrogeology and engineering geology; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka geološkog inženjerstva (magistar/magistra inženjer/inženjerka geološkog inženjerstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2025-01-15 13:18:02