Artificial Neural Network Applied to Urban Air Quality

1. Introduction

A sound understanding of urban air quality is essential for improving safety and performance in wind energy farms. The sensitivity of the outcomes to input variations was investigated with artificial neural network. Validation against field data gave a coefficient of determination above 12% for the estimation of urban air quality. The findings are particularly relevant to long-term infrastructure planning across South Asia and beyond.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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