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Neural Network Approaches for Computation of Soil Thermal Conductivity

Author

Listed:
  • Zarghaam Haider Rizvi

    (Geomechanics & Geotechnics, Kiel University, 24118 Kiel, Germany
    Current address: GeoAnalysis Engineering GmbH, 24118 Kiel, Germany.)

  • Syed Jawad Akhtar

    (Center for Ubiquitous Computing, University of Oulu, 90014 Oulu, Finland
    These authors contributed equally to this work.)

  • Syed Mohammad Baqir Husain

    (Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
    These authors contributed equally to this work.)

  • Mohiuddeen Khan

    (Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India
    These authors contributed equally to this work.)

  • Hasan Haider

    (Department of Information Technology, Krishna Institute of Engineering and Technology, Ghaziabad 201206, India)

  • Sakina Naqvi

    (Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA)

  • Vineet Tirth

    (Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Frank Wuttke

    (Geomechanics & Geotechnics, Kiel University, 24118 Kiel, Germany)

Abstract

The effective thermal conductivity (ETC) of soil is an essential parameter for the design and unhindered operation of underground energy transportation and storage systems. Various experimental, empirical, semi-empirical, mathematical, and numerical methods have been tried in the past, but lack either accuracy or are computationally cumbersome. The recent developments in computer science provided a new computational approach, the neural networks, which are easy to implement, faster, versatile, and reasonably accurate. In this study, we present three classes of neural networks based on different network constructions, learning and computational strategies to predict the ETC of the soil. A total of 384 data points are collected from literature, and the three networks, Artificial neural network (ANN), group method of data handling (GMDH) and gene expression programming (GEP), are constructed and trained. The best accuracy of each network is measured with the coefficient of determination ( R 2 ) and found to be 91.6, 83.2 and 80.5 for ANN, GMDH and GEP, respectively. Furthermore, two sands with 80% and 99% quartz content are measured, and the best performing network from each class of ANN, GMDH and GEP is independently validated. The GEP model provided the best estimate for 99% quartz sand and GMDH with 80%.

Suggested Citation

  • Zarghaam Haider Rizvi & Syed Jawad Akhtar & Syed Mohammad Baqir Husain & Mohiuddeen Khan & Hasan Haider & Sakina Naqvi & Vineet Tirth & Frank Wuttke, 2022. "Neural Network Approaches for Computation of Soil Thermal Conductivity," Mathematics, MDPI, vol. 10(21), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3957-:d:952117
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    References listed on IDEAS

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    1. Shahbaz Ahmad & Zarghaam Haider Rizvi & Joan Chetam Christine Arp & Frank Wuttke & Vineet Tirth & Saiful Islam, 2021. "Evolution of Temperature Field around Underground Power Cable for Static and Cyclic Heating," Energies, MDPI, vol. 14(23), pages 1-19, December.
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