Artificial neural network approach on the seasonal variation of soil resistance

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dc.contributor.author Asimakopoulou, FE en
dc.contributor.author Tsekouras, GJ en
dc.contributor.author Gonos, IF en
dc.contributor.author Stathopulos, IA en
dc.date.accessioned 2014-03-01T02:52:53Z
dc.date.available 2014-03-01T02:52:53Z
dc.date.issued 2011 en
dc.identifier.uri http://hdl.handle.net/123456789/36132
dc.subject.other Artificial intelligence techniques en
dc.subject.other Artificial Neural Network en
dc.subject.other Artificial neural network approach en
dc.subject.other Experimental data en
dc.subject.other Ground resistance en
dc.subject.other Grounding systems en
dc.subject.other Methodological approach en
dc.subject.other Non-linear relationships en
dc.subject.other Rainfall data en
dc.subject.other Seasonal variation en
dc.subject.other Single-value en
dc.subject.other Soil resistivity en
dc.subject.other Lightning en
dc.subject.other Soils en
dc.subject.other Neural networks en
dc.title Artificial neural network approach on the seasonal variation of soil resistance en
heal.type conferenceItem en
heal.identifier.primary 10.1109/APL.2011.6110235 en
heal.identifier.secondary http://dx.doi.org/10.1109/APL.2011.6110235 en
heal.identifier.secondary 6110235 en
heal.publicationDate 2011 en
heal.abstract Objective of this paper is the development of a methodological approach for estimating the ground resistance by using artificial intelligence techniques (specifically, Artificial Neural Network). The value of the ground resistance greatly depends on the grounding system and the properties of the soil, where the system is embedded. Given that the value of soil resistivity fluctuates during the year, the ground resistance does not have one single value. The approach proposed in this paper, takes advantage of the capability of artificial neural networks (ANNs) to recognize linear and non-linear relationships between various parameters. By taking into account measurements of resistivity and rainfall data accrued for previous days, the ground resistance is estimated. On that purpose ANNs have been trained and validated by using experimental data in order to examine their ability to predict the ground resistance. The results prove the effectiveness of the proposed methodology. © 2011 IEEE. en
heal.journalName 2011 7th Asia-Pacific International Conference on Lightning, APL2011 en
dc.identifier.doi 10.1109/APL.2011.6110235 en
dc.identifier.spage 794 en
dc.identifier.epage 799 en

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