> Detailanzeige

Rizvi, Zarghaam Haider [Verfasser:in]; Akhtar, Syed Jawad [Verfasser:in]; Husain, Syed Mohammad Baqir [Verfasser:in]; Khan, Mohiuddeen [Verfasser:in]; Haider, Hasan [Verfasser:in]; Naqvi, Sakina [Verfasser:in]; Tirth, Vineet [Verfasser:in]; Wuttke, Frank [Verfasser:in]

Neural Network Approaches for Computation of Soil Thermal Conductivity

Literatur-
verwaltung

Zur
Merkliste

Lösche von
Merkliste

Per Whatsapp teilen

Schließen

Merkliste



Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
  • Medientyp: Sonstige Veröffentlichung; E-Artikel
  • Titel: Neural Network Approaches for Computation of Soil Thermal Conductivity
  • Beteiligte: Rizvi, Zarghaam Haider [Verfasser:in]; Akhtar, Syed Jawad [Verfasser:in]; Husain, Syed Mohammad Baqir [Verfasser:in]; Khan, Mohiuddeen [Verfasser:in]; Haider, Hasan [Verfasser:in]; Naqvi, Sakina [Verfasser:in]; Tirth, Vineet [Verfasser:in]; Wuttke, Frank [Verfasser:in]
  • Erschienen: MACAU: Open Access Repository of Kiel University, 2022
  • Sprache: Englisch
  • DOI: https://doi.org/10.3390/math10213957
  • Schlagwörter: artificial intelligence ; artificial neural network ; gene expression programming ; effective thermal conductivity ; ScholarlyArticle ; article ; Published Version ; group method of data handling
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: 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 (R2) 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%.
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung (CC BY)