Predicting future global temperature and greenhouse gas emissions via LSTM model

Predicting future global temperature and greenhouse gas emissions via LSTM model

Abstract This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–...

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Bibliographic Details
Main Authors: Ahmad Hamdan, Ahmed Al-Salaymeh, Issah M. AlHamad, Samuel Ikemba, Daniel Raphael Ejike Ewim
Format: Article
Language:English
Published: SpringerOpen 2023-12-01
Series:Sustainable Energy Research
Subjects:
Climate change
Artificial intelligence
Global temperature
Carbon dioxide emissions
Weather forecasting
Recurrent Neural Network
Online Access:https://doi.org/10.1186/s40807-023-00092-x

Internet

https://doi.org/10.1186/s40807-023-00092-x

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