NoSQL database for air quality prediction

  • 1 University of Chemical Technology and Metallurgy, Sofia, Bulgaria


The increasing application of NoSQL database technology and the neural networks raises the question of how compatible and applicable are the NoSQL databases to the neural network prediction models. This paper examines the applicability of a traditional relational database for storing air quality data and compares it to a NoSQL database performing the same functions. The possibility of the NoSQL database to feed a neural network model for predicting the atmospheric air quality is evaluated. The tendencies in the data are studied, and some solutions for improving the air quality are proposed. An analysis and a comparison of the performance of both relational SQL and NoSQL database systems by using real-world data for the Air Quality Index in the city of London is assessed and their performance is compared. Bivariate analysis on the data in order to assess the quality of the neural network forecast is performed.



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