By Mukesh Khare
Artificial neural networks (ANNs), that are parallel computational versions, comprising of interconnected adaptive processing devices (neurons) have the potential to foretell adequately the dispersive habit of vehicular toxins below advanced environmental stipulations. This ebook goals at describing step by step approach for formula and improvement of ANN dependent vice president versions contemplating meteorological and site visitors parameters. The version predictions are in comparison with latest line resource deterministic/statistical dependent versions to set up the efficacy of the ANN strategy in explaining common dispersion complexities in city areas.
The booklet is particularly important for hardcore execs and researchers operating in difficulties linked to city pollution administration and control.
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Additional resources for Artificial Neural Networks in Vehicular Pollution Modelling
His experimental results, when compared with HIWAY-2 predictions, show that HIWAY-2 under-predicts the pollutant concentrations. Sculley  has reviewed four representative approaches namely, IIM, MICRO2, TEXIN2 and CALINE4. The study suggests an alternative emission analysis procedure, which can be used in standard line source models to estimate the dispersion at the intersections. Using historical meteorological and vehicular data, Cooper  has derived ‘meteorological persistence factor’ (MPF) and ‘vehicular persistence factor’ (VPF) for the Florida City.
Peters and Klinzing  describe two separate equations for ground level as well as elevated line source, and analyze the effects of diffusion coefficient in line source dispersion. Using diffusion equation, Lamb and Neiburger  have come out with a model for computing pollutant concentrations resulting from both point and line sources. Later, this model has been tested with respect to its diffusion characteristics by computing the hourly CO concentrations on a particular day, at 760 locations in the Los Angeles basin.
Khare and Sharma  have developed the Delhi finite line source model (DFLSM), (a deterministic based model), for Delhi traffic conditions. This model shows better prediction accuracy for CO when compared to the GFLSM . The formulation of DFLSM has been discussed in Appendix-A. In an another study, Sivacoumar and Thanasekaran,  have applied GFLSM to predict CO concentrations at four sections of major highway in Madras. The model results are comparable to measured CO concentrations. Goyal and Ramakrishna  have developed the Gaussian based finite line source model for describing downwind dispersion of CO in urban roads.