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- W3082256812 abstract "Particulate matter 2.5 (PM2.5) pollution is an actual problem in the modern world and forecasting of the daily concentration of PM25 is a challenging task for researchers. In this study, a novel neural network model that effectively forecasts daily PM2.5 in Hangzhou city was developed in the form of a restricted Boltzmann machines double layer back propagation neural network model (RBM-DL-BPNN). Air quality index, the air pollutants, e.g., particulate matter 10 (PM10), PM25, SO2, CO, NO2, O3, and meteorological parameters (temperature, dew point, humidity, pressure, wind speed, and precipitation) of Hangzhou city were used in this study to train and test three models: RBM-DL-BPNN, double layer back propagation neural network (DL-BPNN), and back propagation neural network (BPNN). The results of experiments and analyses performed indicate that RBM-DL-BPNN has a smaller mean absolute percent error (MAPE), smaller overall daily absolute percentage errors, and more results in terms of absolute percentage error within the range 0-50 % than DL-BPNN and BPNN." @default.
- W3082256812 created "2020-09-08" @default.
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- W3082256812 date "2017-10-03" @default.
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- W3082256812 title "Двошарова нейронна мережа зворотного поширення на основі обмежених машин Больцмана для прогнозування добової концентрації ультрадисперсних частинок РМ2.5" @default.
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- W3082256812 doi "https://doi.org/10.35681/1560-9189.2017.19.3.126541" @default.
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