A Soft Computing Method for Efficient Modelling of Smart Cities Noise Pollution

Noise pollution is one of the most relevant problems in urban area. The main source of noise pollution is the number and type of motor vehicles, but other parameters depending on street configuration yield to a system hardly to be exactly modelled by classical mathematical methods. Smart cities are expected to dynamically control the urban traffic to reduce not just traffic jams, but also to ensure a comfortable noise level for inhabitants. This article gives a design method for efficient genetic fuzzy modelling of traffic generated smart cities noise pollution based on fuzzy logic, multi objective genetic algorithm, gradient descent optimisation and singular value decomposition in the MATLAB environment. Genetic algorithms with objectives to minimise the maximum absolute identification error, the root mean square of the identification error, reduce model complexity and ensure maximal numerical robustness are applied to Zadeh type fuzzy partition membership function parameters preliminary identification, and then gradient descent method is used for their fine-tuning optimization, while the fuzzy rule consequence linear parameters are calculated by singular value decomposition method to find the least squares optimal training data fitting of the model. The training data set is built from measured data, combined with carefully selected simulation data to ensure the completeness of the model and its numerical robustness. Detailed analysis of the method and results by computer simulation of the identification process show the validity of the proposed method.