ArticleScientific reports2024
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Economic and environmental assessment of different energy storage methods for hybrid energy systems.Scientific reports · 2025Article
- Porosity prediction of tight reservoir rock using well logging data and machine learning.Scientific reports · 2025Article
- Characterization and assessment of hydrogen leakage mechanisms in salt caverns.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify the most suitable locations for storing hydrogen in salt caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms-KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, and MLR-creating rock salt deposit suitability maps for hydrogen storage. The performance of these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R
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Registered trials
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