Abstract
The purpose of this study, by using an artificial intelligent approaches, is to compare a correlation between geophysical and geotechnical parameters. The input variables for this system are the electrical resistivity reading, the water content laboratory measurements. The output variable is water content of soils. In this study, our data sets are clustered into 120 training sets and 28 testing sets for constructing the fuzzy system and validating the ability of system prediction, respectively. Relationships between soil water content and electrical parameters were obtained by curvilinear models. The ranges of our samples are changed between 1 - 50 ohm.m (for resistivity) and 20 - 60 (%, for water content). An artificial intelligent system (artificial neural networks, Fuzzy logic applications, Mamdani and Sugeno approaches) are based on some comparisons about correlation between electrical resistivity and soil-water content, for Istanbul and Golcuk Soils in Turkey.
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References
Liu, N.: Soil and Site Characterization Using Electromagnetic Waves, Ph.D. Thesis, Virginia Polytechnic Institute and State University (2007)
Raisov, O.Z.: Relationship of electrical resistivity and temperature for sod-serozemic solonchak. Vestnik MGU. Biology, Soil Science. N 3. Moscow University Press, Moscow (1973). (in Russian)
Ananyan, A.A.: Permafrost studies, vol. 1. Academy of Science of USSR Press, Moscow (1961). (in Russian)
Abidin, M.H.Z., Saad, R., Ahmad, F., Wijeyesekera, D.C., Yahya, A.S.: Soil moisture content and density prediction using laboratory resistivity experiment. Int. J. Eng. Technol. 5, 731–735 (2013)
Pozdnyakova, L.A.: Electrical Properties of Soils. Ph.D.Thesis, University of Wyomins, USA (1999)
Ozcep, F., Tezel, O., Asci, M.: Correlation between electrical resistivity and soil-water content: Istanbul and Golcuk. International Journal of Physical Sciences 4(6), 362–365 (2009)
Ball, R.J., Allen, G.J., Carter, M.A., Wilson, A.A., Ince, C., El-Turki, A.: The application of electrical resistance measurements to water transport in lime–masonry systems. Applied, Physics A 106(3), 669–677 (2012)
Jang, J., Sun, C., Mizutani, E.: Neuro-fuzzy and Soft Computing. Prentice Hall (1997)
Osman, O., Albora, A.M., Ucan, O.N.: Forward Modeling with Forced Neural Networks for Gravity Anomaly Profıle. Mathematical Geology 39(6), 593–605 (2007)
Islam, T., Chik, Z.: Improved near surface soil characterizations using a multilayer soil resistivity model. Geoderma 209, 136–142 (2013)
Hazreek, Z.A., Aziman, M., Azhar, A.T.S., Chitral, W.D., Fauziah, A.M., Rosli, A.S.: The behaviour of laboratory soil electrical resistivity value under basic soil properties influences. In: IOP Conference Series: Earth and Environmental Science, vol. 23(1), p. 012002. IOP Publishing (2015)
Islam, S., Chik, Z., Mustafa, M.M., Sanusi, H.: Model with artificial neural network to predict the relationship between the soil resistivity and dry density of compacted soil. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 25(2), 351–357 (2013)
Abidin, Z., Hazreek, M., Azhar, A.T.S.: The behaviour of laboratory soil electrical resistivity value under basic soil properties influences. Earth and Environmental Science 25(1) (2015)
Charniak, E., McDermott, D.: Introduction to artificial intelligence. Addison (1985)
Brown, M.P., Poulton, M.M.: Locating buried objects for environmental site investigations using neural networks. JEEG 1, 179–188 (1996)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)
Albora, A.M., Özmen, A., Ucan, O.N.: Residual separation of magnetic fields using a cellular neural network approach. Pure and Applied Geophysics 158(9–10), 1797–1818 (2001)
Albora, A.M., Ucan, O.N., Ozmen, A., Ozkan, T.: Separation of Bouguer anomaly map using cellular neural network. Journal of Applied Geophysics 46(2), 129–142 (2001)
Albora, A.M., Uçan, O., Aydogan, D.: Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks. Annals of Geophysics 50(5) (2007)
Ozcep, F., Yildirim, E., Tezel, O., Asci, M., Karabulut, S.: Correlation between electrical resistivity and soil-water content based artificial intelligent techniques. International Journal of Physical Sciences 5(1), 047–056 (2010)
Bian, H., Liu, S., Cai, G., Tian, L.: Artificial neural network model for predicting soil electrical resistivity. Journal of Intelligent & Fuzzy Systems 29(5), 1751–1759 (2015)
Chik, Z., Islam, T.: Near surface soil characterizations through soil apparent resistivity: a case study. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 1, pp. 57–60. IEEE (2013)
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Özçep, F., Yıldırım, E., Tezel, O., Aşçı, M., Karabulut, S., Özçep, T. (2016). A Review on Artificial Intelligence Based Parameter Forecasting for Soil-Water Content. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_27
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DOI: https://doi.org/10.1007/978-3-319-41920-6_27
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