首页|Researchers from National Scientific and Technical Research Council (CONICET) Detail Research in Machine Learning (Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques)
Researchers from National Scientific and Technical Research Council (CONICET) Detail Research in Machine Learning (Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques)
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NETL
NSTL
Elsevier
Current study results on artificial intelligence have been published. According to news reporting from the National Scientific and Technical Research Council (CONICET) by NewsRx journalists, research stated, “Cyclists are particularly vulnerable to travel-related exposure to air pollution. Under- standing the factors that increase exposure is crucial for promoting healthier urban environments.” Funders for this research include Agencia Nacional De Promocion Cientifica Y Tecnologica; Fondo Para La Investigacion Cientifica Y Tecnologica. The news correspondents obtained a quote from the research from National Scientific and Technical Research Council (CONICET): “Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 mg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets.”
National Scientific and Technical Research Council (CONICET)CyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
