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Yousef Rashidi

Researcher at Shahid Beheshti University

Publications -  32
Citations -  463

Yousef Rashidi is an academic researcher from Shahid Beheshti University. The author has contributed to research in topics: Air quality index & Environmental science. The author has an hindex of 9, co-authored 23 publications receiving 325 citations. Previous affiliations of Yousef Rashidi include Esri & Islamic Azad University.

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Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

TL;DR: In this article, an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations is presented.
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A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran

TL;DR: The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3.
Journal Article

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

TL;DR: In this article, a method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations is presented.
Journal ArticleDOI

Identification of dust storm origin in South -West of Iran.

TL;DR: The findings showed the possible correlation between the degree of anthropogenic soil pollutants, and the remains of Iraq-Iran war.
Journal ArticleDOI

Global air quality and covid-19 pandemic: Do we breathe cleaner air?

Mehdi Amouei Torkmahalleh, +53 more
TL;DR: The authors of as discussed by the authors truly appreciate the funding provided by Nazarbayev University through the Collaborative Research Grant (grant number: 091019CRP2104), and they would like to acknowledge the resources provided by CART and EREC to conduct this research.