Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5-Reference-Cited by-同舟云学术

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

Published:2016-10 Issue: Volume:142 Page:465-474
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Ausati Shadi,Amanollahi Jamil

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference56 articles.

1. Prediction of PM10 and TSP air pollution parameters using artificial neural network autoregressive, external input models: a case study in salt, Jordan;Alkasassbeh;Middle-East J. Sci. Res.,2013

2. Effect of the temperature variation between Mediterranean Sea and Syrian deserts on the dust storm occurrence in the western half of Iran;Amanollahi;Atmos. Res.,2015

3. Accuracy assessment of moderate resolution image spectroradiometer products for dust storms in semiarid environment;Amanollahi;Int. J. Environ. Sci. Technol.,2011

4. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization;Antanasijevic;Sci. Total. Environ.,2013

5. A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5;Baker;Atmos. Environ.,2011

Cited by 106 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Utilizing Machine Learning for air pollution prediction, comprehensive impact assessment, and effective solutions in Kolkata, India;Results in Earth Sciences;2024-12

2. Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research;Atmospheric Pollution Research;2024-10

3. A new hybrid deep neural network for multiple sites PM2.5 forecasting;Journal of Cleaner Production;2024-10

4. An Accurate and Efficient Forecast Framework for Fine PM2.5 Maps Using Spatiotemporal Recurrent Neural Networks;Journal of Cleaner Production;2024-09

5. Investigation of models predicting NOx level in the sample region and the use of intelligent transportation system;Environmental Challenges;2024-08