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Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland / S. Chattopadhyay in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4471 - 4482 Titre : Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland Type de document : Article/Communication Auteurs : S. Chattopadhyay, Auteur ; G. Bandyopadhyay, Auteur Année de publication : 2007 Article en page(s) : pp 4471 - 4482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] ozone [Termes IGN] Perceptron multicouche [Termes IGN] pollution atmosphérique [Termes IGN] prédiction [Termes IGN] réseau neuronal artificiel [Termes IGN] Suisse Résumé : (Auteur) The present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Network models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with a learning rate of 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found to be skillful. But the Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period. Copyright Taylor & Francis Numéro de notice : A2007-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250440 En ligne : https://doi.org/10.1080/01431160701250440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28811 [article]

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