IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1620-d1059483.html
   My bibliography  Save this article

Predicting Terrestrial Heat Flow in North China Using Multiple Geological and Geophysical Datasets Based on Machine Learning Method

Author

Listed:
  • Shan Xu

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
    Institute of Geophysics, ETH Zurich, 8092 Zurich, Switzerland)

  • Chang Ni

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Xiangyun Hu

    (School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

Abstract

Geothermal heat flow is an essential parameter for the exploration of geothermal energy. The cost is often prohibitive if dense heat flow measurements are arranged in the study area. Regardless, an increase in the limited and sparse heat flow observation points is needed to study the regional geothermal setting. This research is significant in order to provide a new reliable map of terrestrial heat flow for the subsequent development of geothermal resources. The Gradient Boosted Regression Tree (GBRT) prediction model used in this paper is devoted to solving the problem of an insufficient number of heat flow observations in North China. It considers the geological and geophysical information in the region by training the sample data using 12 kinds of geological and geophysical features. Finally, a robust GBRT prediction model was obtained. The performance of the GBRT method was evaluated by comparing it with the kriging interpolation, the minimum curvature interpolation, and the 3D interpolation algorithm through the prediction performance analysis. Based on the GBRT prediction model, a new heat flow map with a resolution of 0.25°×0.25° was proposed, which depicted the terrestrial heat flow distribution in the study area in a more detailed and reasonable way than the interpolation results. The high heat flow values were mostly concentrated in the northeastern boundary of the Tibet Plateau, with a few scattered and small-scale high heat flow areas in the southeastern part of the North China Craton (NCC) adjacent to the Pacific Ocean. The low heat flow values were mainly resolved in the northern part of the Trans-North China Orogenic belt (TNCO) and the southmost part of the NCC. By comparing the predicted heat flow map with the plate tectonics, the olivine-Mg#, and the hot spring distribution in North China, we found that the GBRT could obtain a reliable result under the constraint of geological and geophysical information in regions with scarce and unevenly distributed heat flow observations.

Suggested Citation

  • Shan Xu & Chang Ni & Xiangyun Hu, 2023. "Predicting Terrestrial Heat Flow in North China Using Multiple Geological and Geophysical Datasets Based on Machine Learning Method," Energies, MDPI, vol. 16(4), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1620-:d:1059483
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1620/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1620/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    3. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    4. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    5. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    6. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    7. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    8. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    9. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    10. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    11. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    12. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    13. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    14. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    15. Catherine Ikae & Jacques Savoy, 2022. "Gender identification on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 58-69, January.
    16. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    17. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
    18. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    19. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
    20. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1620-:d:1059483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.