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- W3199260098 abstract "Nanoparticles generated from urban areas have remarkable effect on environment, climate change and human health including cardiovascular and respiratory problems, among others. Compared to the high cost and difficulty of real-time measurements, statistical models are the most recommended alternative to forecast the exhaust particle number concentrations (PNCs) from urban road traffic. The goal of this research is to forecast vehicle exhaust PNCs using two different methods of Artificial Neural Networks (ANNs) namely, Multi-Layer Perceptron (MLP) and Generalized Regression Neural Network (GRNN), based on continuous real-time measurements. In fact, these measurements were measured using a native algorithm based on the GRIMM analyzer, series 1.108 Portable Aerosol Spectrometer. Besides, this study tends to compare the two chosen methods (GRNN and MLP) in order to distinguish the most suitable method for estimating vehicle exhaust PNCs. The estimated models efficiency was determined by statistical metrics in testing and training phases. The results revealed that GRNN provided the best performance as compared to MLP model, with coefficient of determination R2 equal to 0.98 and 0.80 respectively. In addition, the results are robust enough for correct and accurate next day forecasting of vehicle exhaust PNCs on French urban areas and ensure a sustainable environment and mobility." @default.
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- W3199260098 date "2021-09-21" @default.
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- W3199260098 title "Predicting of Particle Exhaust-Emissions from Urban Road Traffic Using Artificial Neural Networks (ANNs)" @default.
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- W3199260098 doi "https://doi.org/10.1007/978-3-030-84958-0_39" @default.
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