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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 17 > No. 5

 
   

Air Quality Forecasting

PP: 993-1004
doi:10.18576/amis/170556
Author(s)
M Amuthasurabi, VG Gokulanandhanan, ND Hareesh Kumaran, SD Kaushik, P Abiram,
Abstract
By predicting the air quality index for a certain place using machine learning, we forecast India's air quality. The Indian Air Quality Index is a commonly used indicator of pollutant (so2, no2, rspm, spm, etc.) levels across time. We created a model to forecast the air quality index based on historical data from prior years and forecasting over a specific forthcoming year as a gradient descent boosted multivariable regression problem. By using cost estimation for our forecasting problem, we increase the model's effectiveness. With the aid of historical data on pollutant concentration, our model will be able to accurately estimate the air quality index for an entire county, any state, or any contiguous area. By including the suggested parameter-reducing formulations into our model, we outperformed the traditional regression models in terms of performance. Our model predicts the air quality index for the entirety of India with 96% accuracy, and we also utilize the AHP MCDM technique to determine the order of preference based on how closely it resembles the ideal solution.

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