Neural network-based metamodelling approach for estimation of air pollutant profiles

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The air quality system is a system characterised by non-linear, complex relationships. Among existing air pollutants, the ozone (O3), known as a secondary pollutant gas, involves the most complex chemical reactions in its formation, whereby a number of factors can affect its concentration level. To assess the ozone concentration in a region, a measurement method can be implemented, albeit only at certain points in the region. Thus, a more complicated task is to define the spatial distribution of the ozone level across the region, in which the deterministic air quality model is often used by the authority. Nevertheless, simulation by using a deterministic model typically needs high computational requirements due to the nonlinear nature of chemical reactions involved in the model formulation, which is also subject to uncertainties. In the context of ozone as an air pollutant, the determination of the background ozone level (BOL), independent from human activities, is also important as it could represent one of reliable references to human health risk assessment. The concept of BOL may be easily understood, but practically, it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method is presented in this work, by evaluating the relationship of ozone versus nitric oxide (O3-NO) to estimate the BOL value, mainly by using night-time and early morning measurement data collected at the monitoring stations. In this thesis, to deal with the challenging problem of air pollutant profile estimation, a metamodel approach is suggested to adequately approximate intrinsically nonlinear and complex input-output relationships with significantly less computation. The intrinsic characteristics of the underlying physics are not assumed to be known, while the system’s input and output behaviours remain essential. A considerable number of metamodels approach have been proposed in the literature, e.g. splines, neural networks, kriging and support vector machine. Here, the radial basis function neural network (RBFNN) is concerned as it is known to offer good estimation performance on accuracy, robustness, versatility, sample size, efficiency, and simplicity as compared to other stochastic approaches. The development requirements are that the proposed metamodels should be capable of estimating the ozone profiles and its background level temporally and spatially with reasonably good accuracies, subject to satisfying some statistical criteria. Academic contributions of this thesis include in a number of performance enhancements of the RBFNN algorithms. Generally, three difficulties involved in the network training, selection of radial basis centres, selection of the basis function variance (i.e. spread parameter), and training of network weights. The selection of those parameters is very crucial, as they directly affect the number of hidden neurons used and also the network overall performance. In this research, some improvements of the typical RBFNN algorithm (i.e. orthogonal least squares) are achieved. First, an adaptively-tuned spread parameter and a pruning algorithm to optimise the network’s size are proposed. Next, a new approach for training the RBFNN is presented, which involves the forward selection method for selecting the radial basis centres. Also, a method for training the network output weights is developed, including some suggestions for estimation of the best possible values of the network parameters by considering the cross-validation approach. For applications, results show that the combination of the proposed paradigm could offer a sub-optimal solution of metamodelling development in the generic sense (by avoiding the iteration process) for a faster computation, which is essential in air pollutant profile estimation.
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