Estimation of soil electrical resistivity using MLP, RBF, ANFIS and SVM approaches
Author Affiliations
- 1Electrical Engineering Departement, Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo
- 2Electrical Engineering Departement, Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo and LAboratoirede Recherche en Sciences de l´Ingénieur (LARSI), Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo
- 3Electrical Engineering Departement, Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo and LAboratoirede Recherche en Sciences de l´Ingénieur (LARSI), Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo
- 4Electrical Engineering Departement, Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo and LAboratoirede Recherche en Sciences de l´Ingénieur (LARSI), Ecole Nationale Supérieure d´Ingénieurs (ENSI), University of Lome, Togo
Res. J. Recent Sci., Volume 8, Issue (4), Pages 20-30, October,2 (2019)
Abstract
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 Lomé 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.
References
- Choi J.H. and Lee B.H. (2012)., An analysis of conventional grounding impedance based on the impulsive current distribution of a horizontal electrode., Electric Power Systems Research, 85, 30-37. http://dx.doi.org/10.1016/j.epsr.2011.07.005
- Rashad Mohammedeen Kamel, Aymen Chaouachi and 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
- Androvitsaneas V.P., Gonos I.F. and Stathopulos I.A. (2014)., Artificial neural network methodology for the estimation of ground enhancing compounds resistance., IET Science, Measurement & Technology, 8(6), 552-570. http://dx.doi.org/10.1049/iet-smt.2013.0292
- Lee J.P., Ji P.S., Lim J.Y., Kim S.S., Ozdemir A. and Singh C. (2005)., Earth parameter and equivalent resistivity estimation using ANN., In IEEE Power Engineering Society General Meeting, 2597-2602. IEEE. http://dx.doi.org/10.1109/PES.2005.1489485
- 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
- Asimakopoulou F.E., Tsekouras G.J., Gonos I.F., Moronis A.X. and Stathopulos I.A. (2010)., An artificial neural network for estimating the ground resistance., In International Conference on Grounding and Earthing & 4th International Conference on Lightning Physics and Effects.
- Afa J.T. and Anaele C.M. (2010)., Seasonal variation of soil resistivity and soil temperature in Bayelsa State., Am. J. Eng. Appl. Sci, 3(4), 704-709. http://dx.doi.org/10.3844/ ajeassp.2010.704.709
- 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.
- Mehdaoui A., Chaker A., Zerikat M. and Messikh L. (2009)., Development of two neuro-fuzzy models for thecontinuation of the MPPT of the photovoltaic modules UDTS-50 Application to the Adrar site., Revue des Energies Renouvelables, 12(2), 257-268.
- Zaki Abda, Mohamed Chittih and Bilel Zerouali (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 October.
- Mohammadi K., Shamshirband S., Tong C.W., Alam K.A. and Petković D. (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
- Schmitt A., Le Blanc B., Corsini M.M., Lafond C. and Bruzek J. (2001)., Artificial neural networks. A promising data processing tool for anthropology., Bulletins and memoirs of the Anthropology Society of Paris, 13(1-2).
- Warren S. McCulloch and Walter Pitts (1943)., A logical calculus of the ideas immanent in nervous activity., The bulletin of mathematical biophysics, 5(4), 115-133.
- Gronarz T., Habermehl M. and Kneer R. (2016)., Modeling of particle radiation interaction in solid fuel combustion with artificial neural networks., Journal of Power Technologies, 96(3), 206-211.
- Roger Jang J. S. and Sun C. T. (1993)., Functional equivalence between radial basis function networks and fuzzy inference systems., IEEE Trans. on Neural Networks, 4(1), 156-159.
- Alby S. and Shivakumar B.L. (2018)., A prediction model for type 2 diabetes using adaptive neuro-fuzzy interfacesystem., Biomedical Research, S69-S74. http://dx.doi.org/10.4066/biomedicalresearch.29-17-254
- Corinna Cortes and Vladimir Vapnik (1995)., Support-Vector Networks., Machine Learning, 20(3), 273-297. John Platt (1998).
- Platt J. (1998)., Sequential minimal optimization: A fast algorithm for training support vector machines., https://www.microsoft. com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/
- 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/10.1109/ NNSP.1997.622408