An evaluation of CNN and ANN in prediction weather forecasting: A review
Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Research and Development Academy
2021-10-01
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| Series: | Sustainable Engineering and Innovation |
| Online Access: | https://sei.ardascience.com/index.php/journal/article/view/146 |
| _version_ | 1831657773324042240 |
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| author | Shahab Kareem Zhala Jameel Hamad Shavan Askar |
| author_facet | Shahab Kareem Zhala Jameel Hamad Shavan Askar |
| author_sort | Shahab Kareem |
| collection | DOAJ |
| description | Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice. |
| first_indexed | 2024-12-19T17:33:05Z |
| format | Article |
| id | doaj.art-11ab1e140d3c43b5b0793e509fd6db8e |
| institution | Directory Open Access Journal |
| issn | 2712-0562 |
| language | English |
| last_indexed | 2024-12-19T17:33:05Z |
| publishDate | 2021-10-01 |
| publisher | Research and Development Academy |
| record_format | Article |
| series | Sustainable Engineering and Innovation |
| spelling | doaj.art-11ab1e140d3c43b5b0793e509fd6db8e2022-12-21T20:12:24ZengResearch and Development AcademySustainable Engineering and Innovation2712-05622021-10-013214815910.37868/sei.v3i2.id146112An evaluation of CNN and ANN in prediction weather forecasting: A reviewShahab Kareem0Zhala Jameel Hamad1Shavan Askar2Erbil Polytechnic UniversityErbil Polytechnic UniversityErbil Polytechnic UniversityArtificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.https://sei.ardascience.com/index.php/journal/article/view/146 |
| spellingShingle | Shahab Kareem Zhala Jameel Hamad Shavan Askar An evaluation of CNN and ANN in prediction weather forecasting: A review Sustainable Engineering and Innovation |
| title | An evaluation of CNN and ANN in prediction weather forecasting: A review |
| title_full | An evaluation of CNN and ANN in prediction weather forecasting: A review |
| title_fullStr | An evaluation of CNN and ANN in prediction weather forecasting: A review |
| title_full_unstemmed | An evaluation of CNN and ANN in prediction weather forecasting: A review |
| title_short | An evaluation of CNN and ANN in prediction weather forecasting: A review |
| title_sort | evaluation of cnn and ann in prediction weather forecasting a review |
| url | https://sei.ardascience.com/index.php/journal/article/view/146 |
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