Artificial neural networks for the generation of a conductivity map of the ground

Soteris A. Kalogirou, Georgios A. Florides, Panayiotis D. Pouloupatis, Paul Christodoulides and Josephina Joseph-Stylianou

Renewable Energy, 2015, vol. 77, issue C, 400-407

Abstract: In this paper a neural network is used for the generation of a contour map of the ground conductivity in Cyprus. Archived data of thermal conductivity of ground recorded at 41 boreholes are used for training a multiple hidden layer neural network with feedforward architecture. The correlation coefficient obtained between the predicted and training data set is 0.9657, indicating an accurate mapping of the data. The validation of the network was performed using an unknown dataset. The correlation coefficient for the unknown cases was 0.9553. In order to broaden the database, the patterns used for the validation of the technique were embedded into the training data set and a new training of the network was performed. The correlation coefficient value for this case was equal to 0.9718. A 10 × 10 km grid is then drawn over a detailed topographic map of Cyprus and the various input parameters were recorded for each grid point. This information was then supplied to the trained network and by doing so ground conductivity was predicted at each grid-point. This map will be a helpful tool for engineers in designing geothermal heat pump systems in Cyprus.

Keywords: Geothermal maps; Ground conductivity; Artificial neural networks; Boreholes (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:77:y:2015:i:c:p:400-407

DOI: 10.1016/j.renene.2014.12.033

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