Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and artificial neural network

Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and... In this study, for the first time, the combination of Air Q+ software and wavelet neural network was used to predict the mor‑ tality rate caused by the increase in NO concentration in Tehran. In the combination of these two softwares, the wavelet neural network software was used to predict daily NO concentration based on 12 effective parameters, and then the annual concentration of NO was calculated using the daily concentration of wavelet neural network output. Then, annual con‑ centration of NO was used as the input of Air Q+ software. The mortality rate was calculated by Air Q+ software. In this research, the most appropriate predictive algorithm for neural network was studied and layer recurrent algorithm was the most appropriate algorithm. Then, capability of this network was enhanced to predict future NO concentration by wavelet transformation, and wavelet neural network was designed. Also, NO concentration is predicted for future 47 months by using of the time series of the previous data and the wavelet neural network. Analyzing the sensitivity of mortality resulted from NO concentration was done by using of wavelet neural network and Air Q+ software, and it was concluded that the increase or decrease in the parameters http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Environmental Science and Technology Springer Journals

Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and artificial neural network

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Islamic Azad University (IAU)
Subject
Environment; Environment, general; Environmental Science and Engineering; Environmental Chemistry; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution; Soil Science & Conservation; Ecotoxicology
ISSN
1735-1472
eISSN
1735-2630
D.O.I.
10.1007/s13762-018-1818-4
Publisher site
See Article on Publisher Site

Abstract

In this study, for the first time, the combination of Air Q+ software and wavelet neural network was used to predict the mor‑ tality rate caused by the increase in NO concentration in Tehran. In the combination of these two softwares, the wavelet neural network software was used to predict daily NO concentration based on 12 effective parameters, and then the annual concentration of NO was calculated using the daily concentration of wavelet neural network output. Then, annual con‑ centration of NO was used as the input of Air Q+ software. The mortality rate was calculated by Air Q+ software. In this research, the most appropriate predictive algorithm for neural network was studied and layer recurrent algorithm was the most appropriate algorithm. Then, capability of this network was enhanced to predict future NO concentration by wavelet transformation, and wavelet neural network was designed. Also, NO concentration is predicted for future 47 months by using of the time series of the previous data and the wavelet neural network. Analyzing the sensitivity of mortality resulted from NO concentration was done by using of wavelet neural network and Air Q+ software, and it was concluded that the increase or decrease in the parameters

Journal

International Journal of Environmental Science and TechnologySpringer Journals

Published: Jun 4, 2018

References

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