Neural Network Approaches for Computation of Soil Thermal Conductivity

Zarghaam Haider Rizvi (), Syed Jawad Akhtar, Syed Mohammad Baqir Husain, Mohiuddeen Khan, Hasan Haider, Sakina Naqvi, Vineet Tirth and Frank Wuttke
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Zarghaam Haider Rizvi: Geomechanics & Geotechnics, Kiel University, 24118 Kiel, Germany
Syed Jawad Akhtar: Center for Ubiquitous Computing, University of Oulu, 90014 Oulu, Finland
Syed Mohammad Baqir Husain: Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
Mohiuddeen Khan: Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India
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

Mathematics, 2022, vol. 10, issue 21, 1-17

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%.

Keywords: effective thermal conductivity; artificial neural network; group method of data handling; gene expression programming; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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