Self Organizing Map Of Artificial Neural Network For Groundwater Quality Classification In The F'kirina Plain (oum El Bouaghi Province-ne Of Algeria)
| dc.contributor.author | Ouanes, Miyada | |
| dc.contributor.author | Houha, B. | |
| dc.contributor.author | Elhmaidi, A. | |
| dc.contributor.author | Berrada, M. | |
| dc.contributor.author | Ghedjmis, A. | |
| dc.date.accessioned | 2023-09-11T07:42:51Z | |
| dc.date.available | 2023-09-11T07:42:51Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The topological Self-Organizing Maps of Kohonen and other methods of artificial intelligence are effective tools for modeling and solving environmental problems. In this study, we propose an approach to classify the annual physico-chemical parameters of subterranean waters in the F'kirina plain based on the artificial neural network type. The results obtained demonstrate the presence of 4 classes and make it possible to clearly understand and visualize the spatial and temporal distribution of the physicochemical quality of subterranean waters. Class 1 shows high concentrations for all parameters, whereas class 3 is represented by very low concentrations. | ar |
| dc.identifier.issn | 2170-161X | |
| dc.identifier.issn | 2588-2082 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15514 | |
| dc.language.iso | en | ar |
| dc.publisher | Oum-El-Bouaghi University | ar |
| dc.subject | Clustering | ar |
| dc.subject | Self organizing map | ar |
| dc.subject | ANN | ar |
| dc.subject | Physico-chemical parameters | ar |
| dc.subject | Water quality | ar |
| dc.subject | F’kirina plain | ar |
| dc.title | Self Organizing Map Of Artificial Neural Network For Groundwater Quality Classification In The F'kirina Plain (oum El Bouaghi Province-ne Of Algeria) | ar |
| dc.type | Article | ar |