Atmospheric Pollution Research | |
Prediction of airborne nanoparticles at roadside location using a feedâforward artificial neural network | |
Abdullah N.AlâDabbous1  | |
关键词: Artificial neural network; Prediction; Nanoparticles; Comparative evaluation; Multiâlayer feedâforward network; Backâpropagation training algorithm; | |
DOI : 10.1016/j.apr.2016.11.004 | |
学科分类:农业科学(综合) | |
来源: Dokuz Eylul Universitesi * Department of Environmental Engineering | |
【 摘 要 】
Accurate prediction of nanoparticles is essential to provide adequate mitigation strategies for air quality management. On the contrary to PM10, SO2, O3, NOx and CO, nanoparticles are not routinelyâmonitored by environmental agencies as they are not regulated yet. Therefore, a prognostic supervised machine learning technique, namely feedâforward artificial neural network (ANN), has been used with a backâpropagation algorithm, to stochastically predict PNCs in three size ranges (N5â30, N30â100 and N100â300Â nm). Seven models, covering a total of 525 simulations, were considered using different combinations of the routinelyâmeasured meteorological and five pollutants variables as covariates. Each model included different numbers of hidden layers and neurons per layer in each simulation. Results of simulations were evaluated to achieve the optimum correspondence between the measured and predicted PNCs in each model (namely Models, M1âM7). The best prediction ability was provided by M1 when all the covariate variables were used. The model, M2, provided the lowest prediction performance since all the meteorological variables were omitted in this model. Models, M3âM7, that omitted one pollutant covariate, showed prediction ability similar to M1. The results were within a factor of 2 from the measured values, and provided adequate solutions to PNCs' prognostic demands. These models are useful, particularly for the studied site where no nanoparticles measurement equipment exist, for determining the levels of particles in various size ranges. The model could be further used for other locations in Kuwait and elsewhere after adequate longâterm measurements and training based on the routinelyâmonitored environmental data.
【 授权许可】
CC BY
【 预 览 】
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RO201902193316477ZK.pdf | 1923KB | download |