Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models

References

• Abdel Nour, A. A. 1989. Evaluation of furrow-basin irrigation. M.Sc. thesis, Faculty of Agriculture, University of Khartoum.
   Google Scholar
• Abraham, A., M. Koppen, and K. Franke. 2003. Design and application of hybrid intelligent systems. Amsterdam, the Netherlands: IOS Press.
   Google Scholar
• Al-Janobi, A. A., M. A. Abdulwahed, and A. A. Khaled, 2010. Modeling water infiltration rate under conventional tillage systems on a clay soil using artificial neural networks. Australian Journal of Basic and Applied Sciences 4:3869–3879.
   Google Scholar
• Amini, M., K. C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni, and R. Schulin. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European Journal of Soil Science 53:748–757.
   Google Scholar
• Bell, M. A., and H. van Keulen. 1995. Soil pedotransfer functions for four Mexican soils. Soil Science Society of America Journal 59:865–871.
   Web of Science ®Google Scholar
• Bertrand, A. R. 1965. Rate of water intake in the field. In Methods of soil analysis, part I, 1 ed. C. A. Black et al., 197–209. Madison, Wisc.: ASA and SSSA.
   Google Scholar
• Breeuwsma, A., J. H. M. Wosten, J. J. Vleeshouwer, A. M. van Slobbe, and J. Bouma. 1986. Derivation of land qualities to assess environmental problems from soil surveys. Soil Science Society of America Journal 50:186–190.
   Web of Science ®Google Scholar
• Carpena, O., A. Lux, and K. Vahtras. 1972. Determination of exchangeable calcareous soils. Soil Science 33:194–199.
   Web of Science ®Google Scholar
• Cohen, S., and N. Intrator. 2001. Automatic model selection in a hybrid perceptron/radial network. Information Fusion 4:259–266.
   Google Scholar
• Cohen, S., and N. Intrator. 2003. A study of ensemble of hybrid networks with strong regularization. Multiple Classifier Systems 5:227–235.
   Google Scholar
• Dastorani, M. T, A. Moghadamnia, J. Piri, and M. Rico-Ramirez. 2010. Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monitoring Assessment. 166(1–4): 421–434.
   Google Scholar
• Drake, E. H., and H. L. Motto. 1982. An analysis of the effect of clay and organic matter content on the cation exchange capacity of New Jersey soils. Soil Science 133:281–288.
   Web of Science ®Google Scholar
• Elkhidir, A. M. 1985. Furrow irrigation in cracking clay soils with especial reference to the Sudan. Ph.D. thesis, Civil Engineering Department, Southampton University.
   Google Scholar
• Elramlawi, H. R., H. I. Mohammed, and A. M. E. Mohammed. 2007. Comparison of infiltration measuring techniques for furrow irrigation in cracking clay soil. Journal of Science and Technology 8:7–13.
   Google Scholar
• Fernando, M. J., R. G. Burau, and K. Arulanandam. 1977. A new approach to determination of cation exchange capacity. Soil Science society of America Journal 41:818–820.
   Web of Science ®Google Scholar
• Foody, G. M. 2004. Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing 25:3091–3104.
   Web of Science ®Google Scholar
• Hemmat, A., I. Ahmadi, and A. Masoumi. 2007. Water infiltration and clod size distribution as influenced by ploughshare type, soil water content, and ploughing depth. Biosystems Engineering 97:257–266.
   Web of Science ®Google Scholar
• Jain, A., and A. Kumar. 2006. An evaluation of artificial neural network technique for the determination of infiltration model parameters. Applied Software Computing 6:272–282.
   Web of Science ®Google Scholar
• Kenneth, J., S. Wernter, and J. Macinyre. 2001. Knowledge extraction from radial basis function networks and multilayer perceptrons. International Journal of Computational Intelligence and Applications 3:369–382.
   Google Scholar
• Keshavarzi, A., and F. Sarmadian. 2010. Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity. International Journal of Environmental and Earth Sciences 1:1–7.
   Google Scholar
• Keshavarzi, A., F. Sarmadian, R. Labbafi, and M. Rajabi Vandekhali. 2011. Modeling of soil cation exchange capacity based on fuzzy table look-up scheme and artificial neural network approach. Modern Applied Science 5:1–8.
   Google Scholar
• Kurian, C. P., V. I. George, J. Bhat, and R. S. Aithal. 2006. ANFIS model for the time series prediction of interior daylight illuminance. International Journal of Artificial Intelligence and Machine Learning 6:35–40.
   Google Scholar
• Loh, W., and L. Tim. 2000. A comparison of prediction accuracy, complexity, and training time of 33 old and new classification algorithms. Machine Learning 40:203–238.
   Web of Science ®Google Scholar
• Lotfi Anari, P., H. Sharifi Darani, and A. R. Nafarzadegan. 2011. Application of ANN and ANFIS models for estimating total infiltration rate in arid rangeland ecosystem. Research Journal of Environmental Sciences 5:236–247.
   Google Scholar
• Lowery, B., M. A. Arshad, R. Lal, and W. J. Hickey. 1996. Soil water parameters and soil quality. In Methods for assessing soil quality, ed. J. W. Doran and A. J. Jones, 143–157. Madison, Wisc.: SSSA.
   Google Scholar
• Manrique, L. A., C. A. Jones, and P. T. Dyke. 1991. Predicting cation exchange capacity from soil physical and chemical properties. Soil Science Society of America Journal 50:787–794.
   Web of Science ®Google Scholar
• McBratney, A. B., B. Minasny, S. R. Cattle, and R. W. Vervoort. 2002. From pedotransfer function to soil inference system. Geoderma 109:41–73.
   Web of Science ®Google Scholar
• Minasny, B., and A. B. McBratney. 2002. The neuro-m methods for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal 66:352–361.
   Web of Science ®Google Scholar
• Mohammed, H. I. 1982. Hydraulics of long irrigation with reference to Rahad irrigation project. M.Sc. thesis, Faculty of Agriculture University of Khartoum.
   Google Scholar
• Nelson, D. W., and L. E. Sommers. 1982. Total carbon, organic carbon, and organic matter. In Methods of soil analysis, part 2, 2nd ed., ed. A. L. Page, R. H. Miller, and D. R. Keeney, 539–580. Madison, Wisc.: American Society of Agronomy.
   Google Scholar
• Nestor, L. S. Y. 2006. Modeling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences-Journal-des Sciences Hydrologiques 51:3–20.
   Web of Science ®Google Scholar
• Pachepsky, Y. A., D. Timlin, and G. Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal 60 (3): 727–733.
   Web of Science ®Google Scholar
• Parfitt, R. L., D. J. Giltrap, and J. S. Whitton. 1995. Contribution of organic matter and clay minerals to the cation exchange capacity of soil. Communications in Soil Science and Plant Analysis 26:1343–1355.
   Web of Science ®Google Scholar
• Sahrawat, K. L. 1983. An analysis of the contribution of organic matter and clay to cation exchange capacity of some Philippine soils. Communication in Soil Science and Plant Analysis 14:803–809.
   Web of Science ®Google Scholar
• Simpson, P. K. 1990. Artificial neural system: Foundation, paradigm, application, and implementation. New York: Pergamon Press.
   Google Scholar
• Singh, V. P. 1994. Elementary hydrology. New Delhi: Prentice Hall of India.
   Google Scholar
• Singh, T. N., R. Kanchan, A. K. Verma, and S. Singh. 2003. An intelligent approach for prediction of triaxial properties using unconfined uniaxial strength. Mining Engineering Journal 5:12–16.
   Google Scholar
• Sparks, D. L., A. L. Page, P. A. Helmke, R. H. Leoppert, P. N. Soltanpour, M. A. Tabatabai, G. T. Johnston, and M. E. Ummer. 1996. Methods of soil analysis. Madison, Wisc.: Soil Science Society of America.
   Google Scholar
• USDA. 2010. Keys to soil taxonomy, 11th ed. Washington, D.C.: USDA.
   Google Scholar
• Venkatesan, P., and S. Anitha. 2006. Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current Science 91:1195–1199.
   Web of Science ®Google Scholar
• Yilmaz, I., and O. Kaynar. 2011. Multiple regression, ANN (RBF, MLP), and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications 38:5958–5966.
   Web of Science ®Google Scholar