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Land-use suitability analysis of Belgrade city suburbs using machine learning algorithm
(Institute of geoinformatics VŠB - Technical University of Ostrava, 2013)
This paper treats development issues of the suburban areas of Belgrade city. A considerable growth that the city had experienced has led to excessive consumption of land and also to degradation of the landscape and loss ...
Primena mašinskog učenja za procenu cena i količina radova pri izgradnji stambenih i stambeno-poslovnih objekata
(Udruženje inženjera građevinarstva, geotehnike, arhitekture i urbanista "Izgradnja", 2021)
Ovaj rad se bavi problemom procena potrebnih količina radova, kao i koštanja izgradnje stambenih i stambeno-poslovnih objekata korišćenjem algoritama mašinskog učenja. Osnovni cilj je analiza mogućnosti primene mašinskog ...
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, ...
Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks
(IWA Publishing, 2023)
The objective of this research is to propose a novel framework for assessing the consequences of hazardous events on a water resources system using dynamic resilience. Two types of hazardous events were considered: a severe ...
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 ...
Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
(Građevinar, 2021)
Seven state-of-the-art machine learning techniques for estimation of construction
costs of reinforced-concrete and prestressed concrete bridges are investigated in this
paper, including artificial neural networks (ANN) ...
Prediction of aircraft noise using machine learning
(Silesian University Press, Gliwice, Poland, 2021)
In this paper an attempt has been made to predict and evaluate the aircraft-induced noise using model developed by means of machine learning. First step in the development of the model was to artificially calculate noise ...
Upravljanje rizicima pri izgradnji kapitalnih infrastrukturnih objekata u cilju poboljšanja njihove održivosti / Risk management during planning and construction of large infrastructure projects for improving their sustainability
(Универзитет у Београду, Грађевински факултет, 2015)
Investicioni projekat u građevinarstvu se definiše kao kompleksan tehničko-tehnološki,organizacioni, pravni, ekonomski i finansijski poduhvat koji se sastoji od skupakoordinisanih i kontrolisanih aktivnosti sa jasno ...