Air Quality Prediction in Smart Cities Using Wireless Sensor Network and Associative Models

Resumen

This paper describes an application of Wireless Sensor Network and Associative Models to monitor and forecast air quality in Smart Cities. The modifications that were made to the Gamma Classifier provide the foundation for this proposal. The improved model proposes a different way to measure similarity between patterns in the training set, reduces pattern encoding complexity, and improves forecasting performance on atmospheric data series. Experimental results and comparisons against other time series forecasting algorithms show that the proposed associative algorithm achieves better performance and makes better air quality predictions in urban settings.