Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict soil classification and soil parameters of Khartoum State. The study was based on the available data collected from specified areas in Khartoum, and then the results were compared with data brought from actual boreholes to check the ANN model validity. The results indicate that artificial neural networks are a promising method in predicting soil classification and soil parameters of Khartoum State.
| Published in | Science Research (Volume 2, Issue 3) |
| DOI | 10.11648/j.sr.20140203.13 |
| Page(s) | 43-48 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Artificial Neural Networks, Soil Profile, Soil Parameters, Khartoum, Sudan
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APA Style
Hussein Elarabi, Nahed F. Taha. (2014). Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Science Research, 2(3), 43-48. https://doi.org/10.11648/j.sr.20140203.13
ACS Style
Hussein Elarabi; Nahed F. Taha. Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Sci. Res. 2014, 2(3), 43-48. doi: 10.11648/j.sr.20140203.13
AMA Style
Hussein Elarabi, Nahed F. Taha. Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Sci Res. 2014;2(3):43-48. doi: 10.11648/j.sr.20140203.13
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Download@article{10.11648/j.sr.20140203.13,
author = {Hussein Elarabi and Nahed F. Taha},
title = {Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks},
journal = {Science Research},
volume = {2},
number = {3},
pages = {43-48},
doi = {10.11648/j.sr.20140203.13},
url = {https://doi.org/10.11648/j.sr.20140203.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20140203.13},
abstract = {Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict soil classification and soil parameters of Khartoum State. The study was based on the available data collected from specified areas in Khartoum, and then the results were compared with data brought from actual boreholes to check the ANN model validity. The results indicate that artificial neural networks are a promising method in predicting soil classification and soil parameters of Khartoum State.},
year = {2014}
}
TY - JOUR T1 - Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks AU - Hussein Elarabi AU - Nahed F. Taha Y1 - 2014/06/20 PY - 2014 N1 - https://doi.org/10.11648/j.sr.20140203.13 DO - 10.11648/j.sr.20140203.13 T2 - Science Research JF - Science Research JO - Science Research SP - 43 EP - 48 PB - Science Publishing Group SN - 2329-0927 UR - https://doi.org/10.11648/j.sr.20140203.13 AB - Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict soil classification and soil parameters of Khartoum State. The study was based on the available data collected from specified areas in Khartoum, and then the results were compared with data brought from actual boreholes to check the ANN model validity. The results indicate that artificial neural networks are a promising method in predicting soil classification and soil parameters of Khartoum State. VL - 2 IS - 3 ER -
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