Advancing spatiotemporal forecasts of CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si72.svg" display="inline" id="d1e380"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> plume migration using deep learning networks with transfer learning and interpretation analysis-Reference-Cited by-同舟云学术

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Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis

Published:2024-02 Issue: Volume:132 Page:104061
ISSN:1750-5836
Container-title:International Journal of Greenhouse Gas Control
language:en
Short-container-title:International Journal of Greenhouse Gas Control

Author:

Fan Ming^ORCID,Wang Hongsheng,Zhang Jing^ORCID,Hosseini Seyyed A.^ORCID,Lu Dan

Publisher

Elsevier BV

Reference45 articles.

1. Estimating geological CO2 storage security to deliver on climate mitigation;Alcalde;Nat. Commun.,2018

2. Prediction of porous media fluid flow using physics informed neural networks;Almajid;J. Pet. Sci. Eng.,2022

3. A review of developments in carbon dioxide storage;Aminu;Appl. Energy,2017

4. Screening and ranking of sedimentary basins for sequestration of CO2 in geological media in response to climate change;Bachu;Environ. Geol.,2003

5. Principles and practice of explainable machine learning;Belle;Front. Big Data,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A dynamic multi-model transfer based short-term load forecasting;Applied Soft Computing;2024-07

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