首页|Reports Summarize Machine Learning Study Results from University of Belgrade (A Hybrid Suitability Mapping Model Integrating Gis, Machine Learning, and Multi-cr iteria Decision Analytics for Optimizing Service Quality of Electric Vehicle ... )
Reports Summarize Machine Learning Study Results from University of Belgrade (A Hybrid Suitability Mapping Model Integrating Gis, Machine Learning, and Multi-cr iteria Decision Analytics for Optimizing Service Quality of Electric Vehicle ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Belgrade, Serbia, by N ewsRx journalists, research stated, “Electric vehicles are emerging as sustainab le transportation solutions worldwide. Inadequate electric vehicle charging stat ions (EVCS) hinder their broader adoption.” Financial support for this research came from University of Pardubice. The news correspondents obtained a quote from the research from the University o f Belgrade, “Optimal EVCS site selection is vital, requiring multicriteria decis ion-making (MCDM) analyses and geographic information systems (GIS). The researc h introduces, for the first time in site selection problems, an innovative metho dology that integrates GIS, machine learning, and MCDM, effectively mapping the suitability of EVCS in urban environments. This study aims to fill the gap in ev aluating EVCS placement in densely urbanized areas by adopting a retrospective a pproach to examine both primary and secondary criteria at existing EVCS sites. F ocusing on Prague - a city with a dense EVCS network - it assesses their suitabi lity using various MCDM techniques, representing a significant advance in optimi zing EVCS distribution. Spatial analysis facilitated criteria reclassification, and the random forest (RF) algorithm identified key criteria, particularly trans portation infrastructure and population density. Analytic hierarchy process (AHP ), fuzzy AHP, and stepwise weight assessment ratio analysis (SWARA) are employed to derive criteria weights and suitability maps. Comparative results showed a p redilection towards fuzzy AHP over other MCDM methods for modeling suitability a nalysis for placing EVCS, indicating its marginal effectiveness with the largest high-suitability area (172 km 2 ) and hosting the most EVCS (461) in this zone with the highest average score (4.49). This study not only assesses criteria imp ortance and technique efficacy but also signifies a paradigm shift in MCDM from subjective to objective, data -driven decision-making by incorporating machine l earning.”
BelgradeSerbiaEuropeCyborgsEmerg ing TechnologiesMachine LearningUniversity of Belgrade
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