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Random Forest Spatial Interpolation
(MDPI, 2020)
For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external ...
Predicting land use change with data-driven models / Predviđanje promena u korišćenju zemljišta primenom modela vođenih podacima (DATA-DRIVEN MODELS)
(Универзитет у Београду, Грађевински факултет, 2014)
One of the main tasks of data-driven modelling methods is to induce arepresentative model of underlying spatial - temporal processes using past dataand data mining and machine learning approach. As relatively new methods,known ...
Modelling extreme values of the total electron content: Case study of Serbia
(Geofizicki Zavod, 2018)
This paper is dedicated to modeling extreme TEC (Total Electron Content) values at the territory of Serbia. For the extreme TEC values, we consider the maximum values from the peak of the 11-year cycle of solar activity ...
Machine Learning Techniques for Modelling Short Term Land-Use Change
(MDPI AG, 2017)
The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, ...
Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest
(MDPI, 2022)
Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this ...