کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495802 862839 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels
چکیده انگلیسی


• Estimation of the spatial distribution of ozone concentrations.
• Neural network-based metamodelling approach.
• Improved technique for selecting the centres of the network radial basis functions.
• Pollutant level as a function of grid coordinates, topographical information, solar radiation and its precursor emission.
• Validity of the proposed method is verified using data collected at monitoring stations in Sydney.

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input–output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM–CTM), and some of those input–output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM–CTM model only, when compared to the measurement data collected at monitoring stations.

In this paper, improved radial basis function networks, featuring the selection of the network centres, are developed and trained by using the meteorological data collected at monitoring stations to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers good predictions of ozone concentrations, as shown in the following spatial distribution for 8-h maximum average of ozone by using RBFNN and TAPM–CTM model wherein the monitoring stations are represented by bullet dots. Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 10, October 2013, Pages 4087–4096
نویسندگان
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