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Title:Razpoznava suše z integracijo senzorskih in rastrskih podatkov : diplomsko delo
Authors:ID Hauko, Luka (Author)
ID Mongus, Domen (Mentor) More about this mentor... New window
Files:.pdf UN_Hauko_Luka_2023.pdf (2,96 MB)
MD5: E6BA85A30FA190B89B87F849C422D00C
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V sklopu diplomskega dela predstavljamo metodo za razpoznavanje suše, ki temelji na integraciji satelitskih podatkov, iz njih izvedenih podatkovnih produktov, kot sta to normaliziran vegetacijski indeks in indeks vlažnostnega stresa, s senzorskimi podatki o vremenu, vključno s povprečnimi padavinami, zračno vlažnostjo in temperaturo. Podatke smo pridobili preko aplikacijskih programskih vmesnikov ter jih integrirali v podatkovne zbirke, nad katerimi smo izvedli strojno učenje. Slednje je vključevalo metode k-najbližjih sosedov, podporne vektorje in naključni gozd. Rezultati so pokazali, da v našem primeru dosežemo najvišjo natančnost glede na metriko F1 z uporabo slednjega.
Keywords:suša, strojno učenje, razpoznava
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Hauko]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (IX, 36 f.))
PID:20.500.12556/DKUM-85361 New window
UDC:004.932'1:004.85(043.2)
COBISS.SI-ID:171693827 New window
Publication date in DKUM:05.10.2023
Views:423
Downloads:60
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.08.2023

Secondary language

Language:English
Title:Drought recognition with senzor and raster data integration
Abstract:In the scope of this thesis, we present a method for drought detection based on the integration of satellite data, derived data products, such as the Normalized Difference Vegetation Index and Moisture Stress Index, and weather sensor data, including average precipitation, air humidity, and temperature. The relevant data sources were acquired using application programming interfaces and integrated into a common data representation. The drought detection was conducted using several different algorithms, including K-Nearest Neighbors, Support Vector Machines, and Random Forest. As confirmed by the results, later turned out to be the most efficient in our case in terms of F1 metrics.
Keywords:drought, machine learning, recognition


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