Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals-Reference-Cited by-同舟云学术

Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

Published:2022-11 Issue: Volume:8 Page:7643-7656
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Vo Thanh Hung^ORCID,Safaei-Farouji Majid,Wei Ning^ORCID,Band Shahab S.,Mosavi Amir

Publisher

Elsevier BV

Subject

General Energy

Reference61 articles.

1. Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks;Acosta;IEEE Access,2020

2. Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression;Ahmad;Energy,2018

3. Developing a robust proxy model of CO2 injection: Coupling Box–Behnken design and a connectionist method;Ahmadi;Fuel,2018

4. Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs;Al-Mudhafar;J. Pet. Sci. Eng,2020

5. Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns;Ali;Energy,2021

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