Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3989
Title: Using artificial neural networks for the construction of contour maps of thermal conductivity
Authors: Florides, Georgios A. 
Pouloupatis, Panayiotis 
Iosif-Stylianou, Iosifina 
Kalogirou, Soteris A. 
Christodoulides, Paul 
Keywords: Artificial neural networks
Geothermal maps
Thermal conductivity
Boreholes
Issue Date: 2014
Publisher: WSEAS Press
Source: 15th International Conference on Neural Networks, 2014, Gdansk, Poland, 15-17 May
Abstract: In this paper a neural network is used for the construction of a contour map. The particular case of the thermal conductivity map of the ground of the island of Cyprus is considered, with archived data at a number of boreholes throughout Cyprus being used for training a suitable artificial neural network. The data were randomly divided into a training and a validation dataset for a multiple hidden layer feed-forward architecture. The correlation coefficient obtained between the predicted and the training dataset is 0.966, indicating an accurate mapping of the data, while the validation (unknown) dataset exhibits an also satisfactory correlation coefficient of 0.955. The dataset was broadened by embedding the patterns used for the validation into the training dataset with the correlation coefficient equalling a higher 0.972. The available input parameters were then recorded for each grid point on a detailed topographic map of Cyprus, whereby the neural network was used to predict the thermal conductivity at each point. The coordinates and the estimated conductivity were then used as input to a specialized contour drawing software in order to draw the geothermal contour map.
Description: Book title : Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3989
ISBN: 978-960-474-379-7
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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