Artificial neural networks approach for electrochemical resistivity of highly organic soil

Afshin Asadi, Hossein Moayedi, Bujang B K Huat, Alireza Parsaie, Mohd. Raihan Taha

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

The resistivity of highly organic soils was measured using electrochemical resistivity reactor. Artificial neural networks (ANNs) were developed for the prediction of the resistivity at the different organic content, porosity, water content, and temperature. The results of study revealed that the resistivity of the highly organic soil decreased as the water content or temperature increased. The study showed that the resistivity of highly organic soil was also affected by degree of humification. As the degree of peat humification increased, the resistivity decreased. It was also concluded that the constructed ANNs models exhibited high performance for predicting of the resistivity of the highly organic soils.

Original languageEnglish
Pages (from-to)1135-1145
Number of pages11
JournalInternational Journal of Electrochemical Science
Volume6
Issue number4
Publication statusPublished - Apr 2011

Fingerprint

Neural networks
Soils
Water content
Peat
Porosity
Temperature

Keywords

  • Artificial neural networks
  • Organic soil
  • Resistivity

ASJC Scopus subject areas

  • Electrochemistry

Cite this

Artificial neural networks approach for electrochemical resistivity of highly organic soil. / Asadi, Afshin; Moayedi, Hossein; Huat, Bujang B K; Parsaie, Alireza; Taha, Mohd. Raihan.

In: International Journal of Electrochemical Science, Vol. 6, No. 4, 04.2011, p. 1135-1145.

Research output: Contribution to journalArticle

Asadi, Afshin ; Moayedi, Hossein ; Huat, Bujang B K ; Parsaie, Alireza ; Taha, Mohd. Raihan. / Artificial neural networks approach for electrochemical resistivity of highly organic soil. In: International Journal of Electrochemical Science. 2011 ; Vol. 6, No. 4. pp. 1135-1145.
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