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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1857
DC FieldValueLanguage
dc.contributor.authorLe Hoang Kim Thanhen_US
dc.date.accessioned2024-11-15T04:22:29Z-
dc.date.available2024-11-15T04:22:29Z-
dc.date.issued2024-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1857-
dc.description.abstractWeather prediction is a crucial application in meteorology and has been one of the most scientifically and technologically challenging problems worldwide over the last century. Accurate weather prediction is essential for preparing for both favorable and adverse climatic conditions, particularly in regions like the Mekong Delta, where climate variability has significant impacts on agriculture, fisheries, and daily life. This thesis explores the application of deep neural networks to forecast weather in the Mekong Delta. Specifically, we use convolutional neural networks, long short-term memory networks, iTransformer, and Transformer models to predict temperature using meteorological data for the next hour and the next day. By leveraging these advanced deep learning techniques, we aim to improve the accuracy of weather predictions in this region. We will compare the performance and accuracy of these models to determine the most effective approach for weather forecasting in the Mekong Delta. Our findings contribute to the growing body of research on applying deep learning to meteorology and provide valuable insights for enhancing weather prediction capabilities in climate-sensitive regionsen_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectWeather predictionen_US
dc.subjectForecasting modelsen_US
dc.subjectMekong Deltaen_US
dc.titleStudying deep neural networks applied in forecasting weather a scenario of Mekong Deltaen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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