01May 2019

ESTIMATION OF SOIL ELECTRICAL RESISTIVITY USING MLP, RBF, ANFIS AND SVM APPROACHES.

  • Electrical Engineering Department, Ecole Nationale Superieure dIngenieurs (ENSI), University of Lome, Togo.
  • LAboratoire de Recherche en Sciences de lIngenieur (LARSI), Ecole Nationale Superieure dIngenieurs (ENSI), University of Lome, Togo.
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The knowledge of soil electrical resistivity proves essential for a better earthing in order to ensure the protection of telecommunications and electrical energy networks. This study aims to estimate the value of the electrical resistivity of a site\\\'s soil from soil humidity and ambient temperature. The data used were measured at sites in the city of Lome and its surroundings. We developed models using Artificial Neural Network (precisely Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF)), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). The MAPE (Mean Absolute Percentage Error) errors obtained are 0.0011761% for the MLP model, 0.0719309% for the RBF model, 0.00105% for the ANFIS model and 2.89466% for the SVM model. We can say that the results are satisfactory for all models but the ANFIS model is better, given these performances compared to other models. The latter is then retained for the prediction of soil electrical resistivity.

  1. Schmitt, B. Le Blanc, M.-M. Corsini, C. Lafond et J. Bruzek (2001). Artificial neural network, a relevant data processing tool for anthropology.Bulletins and memoirs of theAnthropology Society of Paris, 13 (1-2) | 2001.
  2. Afa John TARILANYO and Anaele C.M. (2010). Seasonal Variation of Soil Resistivity and Soil Temperature in Bayelsa State. American Journal of Engineering and Applied Sciences, 3(4),704-709http://dx.doi.org/3844/ajeassp.2010.704.709
  3. Anbazhagan S. (2015). Athens Seasonal Variation of Grounding Prediction Using Neural Networks. ICTACT Journal On Soft Computing, 06(1). http://dx.doi.org/10.21917/ijsc.2015.0154
  4. Fani E. Asimakopoulou, George J. Tsekouras, Ioannis F. Gonos, A.X. Moronis, Ioannis A. Stathopulos (2010). An artificial neural network for estimating the ground resistance. International Conference on Grounding and Earthing & 4th International Conference on Lightning Physics and Effects Salvador ? Brazil, November.
  5. P. Lee; P.S. Ji; J.Y. Lim; S.S. Kim; A. Ozdemir; C. Singh (2005).Earth parameter and equivalent resistivity estimation using ANN. IEEE Power Engineering Society General Meeting. http://dx.doi.org/10.1109/PES.2005.1489485
  6. Jong-Hyuk Choi and Bok-Hee Lee 2012. An analysis of conventional grounding impedance based on the impulsive current distribution of a horizontal electrode. Electric Power Systems Research , 85 (2012), 30-37. http://dx.doi.org/10.1016/j.epsr.2011.07.005
  7. KasraMohammadi, Shahaboddin Shams-hirband, Chong Tong Wen, KhubaibAmjadAlam, Dalibor Petkovic (2015). Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year. Energy Conversion and Management, 93, 406-413. http://dx.doi.org/10.1016/j.enconman.2015.01.021
  8. Marcin Grabarczyk and Piotr Furmanski (2013). Predicting the effective thermal conductivity of dry granular media using artificial neural networks. Journal of Power Technologies, 93 (2), 59?66.
  9. Mehdaoui A., Chaker A., Zerikat M. and Messikh L. (2009). Development of two neuro-fuzzy models for the continuation of the MPPT of the photovoltaic modules UDTS-50 Application to the Adrar site. Revue des Energies Renouvelables, 12 (2), 257-268.
  10. OSUNA E., FREUND R., and GIROSI F. (1997). Improved Training Algorithm for Support Vector Machines. Proceedings of the 1997 IEEE Workshop on Neural Network for Signal Processing. http://dx.doi.org/1109/NNSP.1997.622408
  11. RashadMohammedeenKamel, AymenChaouachi, Ken Nagasaka 2011. Comparison the Performances of Three Earthing Systems for Micro-Grid Protection during the Grid Connected Mode. Smart Grid and Renewable Energy, 2(3). http://dx.doi.org/10.4236/sgre.2011.23024
  12. S Alby, BL Shivakumar (2018). A prediction model for type 2 diabetes using adaptive neuro-fuzzy interfacesystem.Biomedical Research, S69-S74. http://dx.doi.org/4066/biomedicalresearch.29-17-254
  13. Tim Gronarz, Martin Habermehl, Reinhold Kneer (2016). Modeling of Particle Radiation Interaction in Solid Fuel Combustion with Artificial Neural Networks. Journal of Power Technologies, 96 (3), 206?211.
  14. Vasilios P. Androvitsaneas, Ioannis F. Gonos, Ioannis A. Stathopulos (2014). Artificial neural network methodology for the estimation of ground enhancing compounds resistance. IET Science, Measurement and Technology, 8(6), 552?570.http://dx.doi.org/1049/iet-smt.2013.0292
  15. ZakiAbda, Mohamed Chittih, BilelZerouali (2015). Modeling extreme flows by artificial neural networks and neuro-fuzzy inference systems (application to Algiers coastal basins). International Conference on African Large Basin Hydrology River Hammamet, Tunisia, 26-30th

[Sangue Oraleou Djandja, Adekunle Akim Salami, Kpomone Komla Apaloo Bara and Koffi-Sa Bedja. (2019); ESTIMATION OF SOIL ELECTRICAL RESISTIVITY USING MLP, RBF, ANFIS AND SVM APPROACHES. Int. J. of Adv. Res. 7 (May). 48-60] (ISSN 2320-5407). www.journalijar.com

Adekunlé Akim Salami
2. LAboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lome, Togo

DOI:

Article DOI: 10.21474/IJAR01/9004      
DOI URL: http://dx.doi.org/10.21474/IJAR01/9004

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