Open Access
| Issue |
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
|
|
|---|---|---|
| Article Number | 04005 | |
| Number of page(s) | 8 | |
| Section | AI & Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202636704005 | |
| Published online | 29 April 2026 | |
- Shi, H., Zhang, Y., Yu, Z. and Yang, Y., 2024. Reservoir temperature prediction based on characterization of water chemistry data: A case study of western Anatolia, Turkey. Scientific Reports, 14, 10339. Duplyakin, D. et al. (2020) Using machine learning to predict temperature outputs in geothermal systems. [Google Scholar]
- Ahmadi, M., 2025. Interpretable machine learning for high-accuracy reservoir temperature prediction in geothermal energy systems. Energies, 18(13), 3366. [Google Scholar]
- Zhou, Z., Wu, Y. and Li, X., 2024. Artificial intelligence applications for accurate geothermal temperature prediction in the lower Friulian Plain (north-eastern Italy). Journal of Cleaner Production, 460, 142452. [Google Scholar]
- Dong, P., Du, L., Zhao, L., Bao, Y. and Yin, M., 2024. Application of machine learning models for groundwater temperature prediction in geothermal development. Earth Science Journal, DOI:10.19509/j.cnki.dzkq.tb20240063. [Google Scholar]
- Al-Fakih, A., Al-khudafi, A., Koeshidayatullah, A., Kaka, S.L. and Al-Gathe, A., 2025. Forecasting geothermal temperature in western Yemen with Bayesian- optimized machine learning regression models. Geothermal Energy, 13(1), pp.1–29. [Google Scholar]
- Duplyakin, D., Beckers, K.F., Siler, D.L., Martin, M. and Johnston, H.E., 2022. Modeling subsurface performance of a geothermal reservoir using machine learning. Energies, 15(3), 967. [Google Scholar]
- Rajabi, M.M. and Chen, M., 2022. Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects. Advances in Geo-Energy Research, 6(6), pp.451–453. [Google Scholar]
- Al-Aghbary, M., Sobh, M. and Gerhards, C., 2022. A geothermal heat flow model of Africa based on random forest regression. Frontiers in Earth Science, 10, 981899. [Google Scholar]
- Brown, S., Rodi, W.L., Seracini, M., Gu, C., Fehler, M., Faulds, J., Smith, C.M. and Treitel, S., 2022. Bayesian neural networks for geothermal resource assessment: Prediction with uncertainty. Geophysics / arXiv preprint. [Google Scholar]
- Chen, G., Luo, X., Jiang, C. and Jiao, J.J., 2022. Surrogate-assisted level-based learning evolutionary search for heat extraction optimization of enhanced geothermal systems. Energy Systems / arXiv preprint. [Google Scholar]
- Santana, A.P., Machado, R.A., Teixeira, V.H.F.L. and De Carvalho, D.O., 2024. Deep neural network applied to predict geothermal potential. In: Proceedings of the EAGE Global Energy Transition Conference & Exhibition, pp.1–5. [Google Scholar]
- Chen, G., Jiao, J.J., Liu, Q., Wang, Z. and Jin, Y., 2024. Machine learning- accelerated multi-objective design of fractured geothermal systems. Renewable Energy Systems / arXiv preprint. [Google Scholar]
- Mejía-Fragoso, J.C., Florez, M.A. and Bernal-Olaya, R., 2024. Predicting the geothermal gradient in Colombia: A machine learning approach. Geoscience Frontiers / arXiv preprint. [Google Scholar]
- Khalaf, M.S., 2026. Physics-based decline curve analysis and machine learning for temperature forecasting in enhanced geothermal systems. Geothermal Energy Systems / arXiv preprint. [Google Scholar]
- Zhang, X. et al., 2025. Prediction of geothermal heat flow for sustainable energy applications with sparse geological data using machine learning. Energy and AI, 22, 100615. [Google Scholar]
- Liu, Y. et al., 2023. Probabilistic geothermal resources assessment using machine learning: Bayesian correction framework based on Gaussian process regression. Geothermics, 114, 102787. [Google Scholar]
- Jia, G., Hao, J., Zhang, Z., Zhang, M. and Jin, L., 2026. Machine learning-based assessment of medium-deep geothermal energy potential in urban systems. Geothermics, 136, 103578. [Google Scholar]
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