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Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions
(Blue-Green Systems, 2023)
The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, ...
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 ...
Compensating the lack of big data in construction industry with expert knowledge: a case study
(1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2022, 2022)
Due to various reasons, there is a lack of big data in the construction industry, one of the main obstacles to a broader implementation of AI. Another obstacle is adhering to analytical methods in fields more suitable for ...
Rapid earthquake loss assessment based on machine learning and representative sampling
(Earthquake Spectra, 2021)
This paper proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A Random Forest classification model predicts a ...
Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge
(Sustainability, 2022)
Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of ...
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) ...
Kartiranje šumske vegetacije na osnovu podataka satelitskog osmatranja Zemlje korišćenjem tehnika mašinskog učenja / Mapping Forest Vegetation from Satellite Earth Observation Data Using Machine Learning Techniques
(2022)
Potrebe za kvalitetnim podacima kvantitativnih i kvalitativnih karakteristika šuma se povećavaju kako je pritisak na ovaj prirodni resurs sve veći. Podaci satelitskog osmatranja Zemlje su se pokazali kao pogodna alternativa ...
Spatio-temporal interpolation of climate elements using geostatistics and machine learning
(Универзитет у Београду, Грађевински факултет, 09-04-2021)
High resolution daily maps for climate elements are a valuable source of information and serve as aninput for climatology, meteorology, agriculture, hydrology, ecology, and many other research areasand disciplines. ...
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 ...
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 ...