Deep-Learning models for daily PM<sub>10</sub> forecasts using feature selection and genetic algorithm-Reference-Cited by-同舟云学术

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Deep-Learning models for daily PM₁₀ forecasts using feature selection and genetic algorithm

Published:2022-10-06 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Optimization and Applications (ICOA)
language:
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Author:

Bouakline Oumaima¹,El Merabet Youssef²,Khomsi Kenza³

Affiliation:

1. Ibn Tofail University,SETIME Laboratory, Faculty of Science,Department of Physics,Kenitra

2. Ibn Tofail University,SETIME Laboratory, Faculty of Science,Department of Physics,Kenitra,Morocco

3. General Directorate of Meteorology,Casablanca,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9933916/9933999/09934503.pdf?arnumber=9934503

Reference10 articles.

1. Application of cellular neural network (CNN) to the prediction of missing air pollutant data

2. Forecasting air quality time series using deep learning

3. Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models

4. PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network

5. Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting

Cited by 2 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. Predicting Particulate Matter (PM10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.;2024-08-02

2. Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-03-14

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