An Intelligent Weather Prediction Model Using Optimized 1D CNN With Attention GRUView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000716</doi_batch_id><timestamp>1716029870</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 2</title></titles><publication_date><month>03</month><year>2024</year></publication_date><journal_volume><volume>26</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>An Intelligent Weather Prediction Model Using Optimized 1D CNN With Attention GRU</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>One of the main factors affecting human livelihoods is weather events. High weather disasters with forest fires, high air temperature, and global warming that cause drought. An efficient and accurate weather forecasting approach is required to take measures against climate disasters. Therefore, it is important to design an approach that makes better weather prediction. This work presents an optimized deep learning model, 1D convolutional neural network (CNN), with an attention gated recurrent unit (GRU) model for reliable weather forecasting. That is, to capture the local features of weather data, 1D CNN is used, and to capture the temporal features of the weather data, an optimized GRU is used. The attention mechanism is used for improving the performance, and the hyperparameter of GRU are optimized by the adaptive wild horse algorithm (AWHA). This work considered the Jena meteorological database which has 14 parameters, and the comparative analysis is carried out for different prediction measures. The proposed weather prediction model achieved better mean square error (MSE) and root mean square (RMSE) values.</p> </p></abstract><publication_date><month>02</month><day>08</day><year>2024</year></publication_date><pages><first_page>1</first_page><last_page>9</last_page></pages><publisher_item><identifier id_type="pii">gnest_05408</identifier></publisher_item><doi_data><doi>10.30955/gnj.005408</doi><timestamp>1716029870</timestamp><resource>https://journal.gnest.org/publication/gnest_05408</resource></doi_data></journal_article></journal></body></doi_batch> </div>
