Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data
The application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/20/e3sconf_evf2021_01006.pdf |
| _version_ | 1811226778337804288 |
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| author | Lazri Mourad Ouallouche Fethi Labadi Karim Ameur Soltane |
| author_facet | Lazri Mourad Ouallouche Fethi Labadi Karim Ameur Soltane |
| author_sort | Lazri Mourad |
| collection | DOAJ |
| description | The application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to the size and dimension of the data used. Contrary to these considerations, a new neural network, such as Extreme Learning Machine (ELM) has recently been implemented. The ELM does not care much about the size of the neural network, the hidden layer parameters are randomly generated and remain constant instead of being adjusted during training. In this paper, we will present a comparison between two neural networks, namely ELM and MLP (Multilayer perceptron) implemented for the precipitation estimation from meteorological satellite data. The architecture chosen for the two neural networks consists of an input layer (7 neurons), a hidden layer (8 neurons) and an output layer (7 neurons). The MLP has undergone standard training as soon as the ELM is trained according to the characteristics mentioned above. The results show that MLP prevails over ELM. However, the time cost during learning is too high for MLP compared to ELM. |
| first_indexed | 2024-04-12T09:30:59Z |
| format | Article |
| id | doaj.art-230c167faec048149d7f82466221d499 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-12T09:30:59Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-230c167faec048149d7f82466221d4992022-12-22T03:38:21ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013530100610.1051/e3sconf/202235301006e3sconf_evf2021_01006Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG DataLazri Mourad0Ouallouche Fethi1Labadi Karim2Ameur Soltane3Laboratoire LAMPA (laboratoire d’analyse et de modélisation des phénomènes aléatoires) Faculty G.E.I, Mouloud MAMMERI University of Tizi-OuzouLaboratoire LAMPA (laboratoire d’analyse et de modélisation des phénomènes aléatoires) Faculty G.E.I, Mouloud MAMMERI University of Tizi-OuzouECAM-EPMI LR2E, / Quartz-Lab (EA7393)Laboratoire LAMPA (laboratoire d’analyse et de modélisation des phénomènes aléatoires) Faculty G.E.I, Mouloud MAMMERI University of Tizi-OuzouThe application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to the size and dimension of the data used. Contrary to these considerations, a new neural network, such as Extreme Learning Machine (ELM) has recently been implemented. The ELM does not care much about the size of the neural network, the hidden layer parameters are randomly generated and remain constant instead of being adjusted during training. In this paper, we will present a comparison between two neural networks, namely ELM and MLP (Multilayer perceptron) implemented for the precipitation estimation from meteorological satellite data. The architecture chosen for the two neural networks consists of an input layer (7 neurons), a hidden layer (8 neurons) and an output layer (7 neurons). The MLP has undergone standard training as soon as the ELM is trained according to the characteristics mentioned above. The results show that MLP prevails over ELM. However, the time cost during learning is too high for MLP compared to ELM.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/20/e3sconf_evf2021_01006.pdf |
| spellingShingle | Lazri Mourad Ouallouche Fethi Labadi Karim Ameur Soltane Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data E3S Web of Conferences |
| title | Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data |
| title_full | Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data |
| title_fullStr | Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data |
| title_full_unstemmed | Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data |
| title_short | Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data |
| title_sort | extreme learning machine versus multilayer perceptron for rainfall estimation from msg data |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/20/e3sconf_evf2021_01006.pdf |
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