Deep numeral models of air quality in the Tula metropolitan area, Hidalgo
DOI:
https://doi.org/10.36825/RITI.13.29.010Keywords:
Air Quality, Deep Learning, Artificial Intelligence, Air Pollution, Environmental MonitoringAbstract
In recent decades, increasing urbanization and industrial development have been a cornerstone of metropolitan development, representing an increase in the production and emission of greenhouse gases. This situation has led to air pollution and an unfavorable environmental situation that could affect ecosystems, the population, and life in general. Therefore, preventive strategies are required. Therefore, two artificial intelligence models were developed to predict air quality in the Tula Metropolitan Area (TMA) using classification and regression algorithms with deep learning neural networks. The CRISP-DM methodology was applied, considering the TMA as the producer of 90% of pollution in Hidalgo. Data understanding and preparation were performed prior to modeling and validating each model. Applying evaluation metrics resulted in an r2 of .99, a loss of 0.088 for the regression model, a .93 accuracy for the classification model, a precision of .88, recall of .79 and F1-score of .81 Both models were able to represent the air quality phenomenon and predict it efficiently.
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