The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5


  • Untung Rahardja University of Raharja
  • Qurotul Aini University of Raharja
  • Po Abas Sunarya University of Raharja
  • Danny Manongga Satya Wacana Christian University
  • Dwi Julianingsih Univeristy of Raharja





 Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables. 


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How to Cite

Rahardja, U. ., Aini, Q. ., Sunarya, P. A. ., Manongga, D. ., & Julianingsih, D. (2022). The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5. Aptisi Transactions on Technopreneurship (ATT), 4(3), 313–324.




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