Journal of Intelligent Systems 28 (5):893-903 (2019)

Sai Ganesh
Andhra University College of Engineering
Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index, artificial neural networks trained with conjugate gradient descent, such as multilayer perceptron, cascade forward neural network, Elman neural network, radial basis function neural network, and nonlinear autoregressive model with exogenous input along with regression models such as multiple linear regression consisting of batch gradient descent, stochastic gradient descent, mini-BGD and CGD algorithms, and support vector regression, are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.
Keywords No keywords specified (fix it)
Categories No categories specified
(categorize this paper)
DOI 10.1515/jisys-2017-0277
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy

Upload a copy of this paper     Check publisher's policy     Papers currently archived: 65,714
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

No references found.

Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

A Step in the Right Direction.Mary Ann Metzger - 1993 - Journal Od Mathematical Psychology 37 (3):477-485.
Out of Their Minds: Legal Theory in Neural Networks. [REVIEW]Dan Hunter - 1999 - Artificial Intelligence and Law 7 (2-3):129-151.
Some Neural Networks Compute, Others Don't.Gualtiero Piccinini - 2008 - Neural Networks 21 (2-3):311-321.
Networks with Attitudes.Paul Skokowski - 2007 - Artificial Intelligence and Society 22 (3):461-470.
Synthetic Neuroethology.Pete Mandik - 2002 - Metaphilosophy 33 (1‐2):11-29.


Added to PP index

Total views
3 ( #1,335,539 of 2,462,723 )

Recent downloads (6 months)
2 ( #299,212 of 2,462,723 )

How can I increase my downloads?


My notes