title: Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.
abstract: Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, which were measured at El Hamma station in Algiers. Artificial neural network models were developed using a combination of meteorological and time-scale as input variables. The results were rather satisfactory, with values of the coefficient of correlation (R (2)) for independent test sets ranging between 0.60 and 0.85 and values of the index of agreement (IA) between 0.87 and 0.96. In addition, the root mean square error (RMSE), the mean absolute error (MAE), the normalized mean squared error (NMSE), the absolute relative percentage error (ARPE), the fractional bias (FB), and the fractional variance (FS) were calculated to assess the performance of the model. It was seen that the overall performance of model 3 was better than models 1 and 2.
authors.affiliation: Faculty of Materials Science and Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia. redachellali@daad-alumni.de.|Laboratory of Environmental Science and Material Studies, University of Oran 1-Ahmed Benbella, Oran, Algeria. redachellali@daad-alumni.de.
authors.forename: M R
authors.initials: MR
authors.lastname: Chellali
authors.affiliation: Laboratory of Environmental Science and Material Studies, University of Oran 1-Ahmed Benbella, Oran, Algeria.|Hydrometeorological Institute for Training and Research-IHFR, Oran, Algeria.
authors.forename: H
authors.initials: H
authors.lastname: Abderrahim
authors.affiliation: Laboratory of Environmental Science and Material Studies, University of Oran 1-Ahmed Benbella, Oran, Algeria.
authors.forename: A
authors.initials: A
authors.lastname: Hamou
authors.affiliation: Institute of Science and Technology, University Center Ain Témouchent, Ain Témouchent, Algeria.
authors.forename: A
authors.initials: A
authors.lastname: Nebatti
authors.affiliation: Faculty of Materials Science and Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia.
authors.forename: J
authors.initials: J
authors.lastname: Janovec
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000462:Algeria; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005544:Forecasting; D055904:Meteorology; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Air pollution; Neural network; PM10 emission forecasting; Particulate matter