Support Vector Machine for Urban Air Quality

1. Introduction

Efficient use of scarce resources in embedded systems demands a deeper understanding of urban air quality. A suitable control strategy was designed using support vector machine and evaluated in detailed numerical simulation. Cost savings of about 47% were estimated for the optimised system over its service life. The results establish a benchmark against which future improvements can be measured.

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2. Materials and Methods

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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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