An Unified Method of Exploration-Based Air Quality Prediction
R. Kavitha1, Thrinath Sirna2, Panini Vashisth3, Rithik Shaw4

1Dr.R.Kavitha, AP/Department of IT, SRMIST, Ramapram, Chennai.
2Thrinath Sirna, UG Student, Department of IT, SRMIST, Ramapram, Chennai.
3Panini Vashisth, UG Student, Department of IT, SRMIST, Ramapram, Chennai.
4Rithik Shaw, UG Student, Department of IT, SRMIST, Ramapram, Chennai.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4705-4708 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9156038620/2020©BEIESP | DOI: 10.35940/ijrte.F9156.038620

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Abstract: Data mining is the application of examining large current databases in sequence to create new information. It is a classification of artificial intelligence build on the concept that systems can get from data, analyze patterns and make judgment with minimal human intervention. The forecast of air quality is done with analyzing the AQI (Air Quality Index) of the atmosphere in different areas. These predictions are done using the BP Neural network Algorithm in which the data of the gases like CO2, CO, SO2, O3, NO2, PM2.5 etc. is first classified in the system, and then the normality is checked by comparison of each gases with the normality. But the prediction cannot be fully excepted because it doesn’t consider the outside weather condition of the atmosphere. This paper uses the ANN (Artificial Neural Network) technique along with BP Neural Network which analysis the weather condition of the atmosphere along with the data of the polluted gases. This paper predict more efficient air quality index of the atmosphere.
Keywords: Data Mining, Back Propagation Neural Network (BCNN), Air Quality Index(AQI)
Scope of the Article: Data Mining.