Document Type : Original Article
Authors
Abstract
Cation exchange capacity (CEC) is one of the most important soil chemical properties that affects other chemical, physical, and biological soil properties and fertility. In this study, performance of some procedures such as regression, Fuzzy and Fuzzy-genetic approaches in estimation of soil CEC has been investigated. Consequently, the required data of 770 samples from the Europe database (IES) was extracted. Then multiple linear regression, Fuzzy and Fuzzy-genetic approaches were used for development of pedotransfer functions for estimating of soil CEC. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) criteria were used for evaluation of the proposed models. The values of R2, RMSE and MAE obtained for the linear regression model 0.72, 7.42 cmolc kg-1 and 9.13 cmolc kg-1, for Fuzzy model, 0.78, 5.44 cmolc kg-1 and 4.32 cmolc kg-1, for Fuzzy-genetic model 0.84, 4.7 cmolc kg-1 and 3.57 cmolc kg-1, respectively. These results indicated that the Fuzzy-genetic CEC model is more accurate than Fuzzy model, and Fuzzy model is more accurate than regression CEC model.
Keywords
Ahmad, S., and Simonovic, S. P. (2005). An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology, 315(1), 236-251.
Altin, A., and Degirmenci, M. (2005). Lead (II) removal from natural soils by enhanced electrokinetic remediation. Science of the Total Environment, 337(1), 1-10.
Amini, M., Abbaspour, K. C., Khademi, H., Fathianpour, N., Afyuni, M., and Schulin, R. (2005). Neural network models to predict cation exchange capacity in arid regions of Iran. European Journal of Soil Science, 56(4), 551-559.
Andriantiatsaholiniaina, L. A., Kouikoglou, V. S., and Phillis, Y. A. (2004). Evaluating strategies for sustainable development: fuzzy logic reasoning and sensitivity analysis. Ecological Economics, 48(2), 149-172.
Ansari, H. and Davary, K. (2010). Estimating Precipitation Data Using a Fuzzy based Technique. Iran-Water Resources Research, 6 (1), 39 -47.
Arias, M., Perez-Novo, C., Osorio F., Lopez E. and Soto, B. (2005). Adsorption and desorption of copper and zinc in the surface layer of acid soils. Journal of Colloid and Interface Science, 288 (1), 21-29.
Asadipour, N., Karami, M. and Shahinezhad, B. (2012). Genetic algorithm using fuzzy logic in determining the parameters for optimizing the outflow hydrograph. Journal of Iran Water Research, 6 (10), 37-45, (In Persian).
Bouma, J. (1989). Using soil survey data for qualitative land evaluation. Advances in Soil Science, 9, 177-213.
Cai, X., McKinney, D. C. and Lasdon, L. S. (2001). Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Advances in Water Resources, 24 (1), 667–676.
Chang, C. L., Lo, S. L. and Yu, S. L. (2005). Applying fuzzy theory and genetic algorithm to interpolate precipitation. Journal of Hydrology, 314 (1-4), 92–104.
Chen, L. (2003). Real coded genetic algorithm optimization of long-term reservoir operation. Environment and Urbanization, 39(5), 1157–1165.
Ducey, M. J. and Larson, B.C. (1999). A fuzzy set approach to the problem of sustainability. Forest Ecology and Management, 115 (1), 29-40.
Freidrich, C., Fohrer, N. and Frede, H. G. (2002). Quantification of soil properties based on external information by means of fuzzy-set theory. Journal of Plant Nutrition and Soil Science, 165 (4), 511-516.
Hajabbasi, M. A., Jalalian, A. and Karimzadeh, H. R. (1997). Deforestation effects on soil physical and chemical properties, Lordegan, Iran. Plant and Soil, 190 (2), 301-308.
Hosseini Arabloo, N., Khodaverdiloo, H., Ghorbani Dashtaki, S. H. and Momtaz, H. R. (2015). Effect of grouping soils based on their organic carbon and clay content on performance of hierarchical pedotransfer functions of soil cation exchange capacity. Journal of Soil Management and Sustainable Production, 4 (4), 215-234, (In Persian).
Ines, A. V. M. and Droogers, P. (2002). Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach. Hydrology and Earth System Sciences. 6 (1), 49-65.
Karimian, A. 1996. The role of clay and organic matter in caion exchange capacity of calcareous soils in Fars province. 5th Soil Science Congress, Karaj, Iran, (In Persian).
Keller, A., von Steiger, B., van Der Zee, S. T. and Schulin, R. (2001). A stochastic empirical model for regional heavy metal balances in agro ecosystems. Journal of Environmental Quality, 30 (6), 1976-1989.
Keshavarzi, A., Sarmadian, F., Labbafi, R. and Rajabi Vandechali, M. (2011). Modeling of Soil Cation Exchange Capacity Based on Fuzzy Table Look-up Scheme and Artificial Neural Network Approach. Journal of Modern Applied Science, 5 (1), 153-164.
Khodaverdiloo, H. and Hosseini Arablu, N., (2014). Derivation, Validation and Comparison of Class and Continuous Pedotransfer functions for Predicting Soil Cation Exchange Capacity in Several Textural Classes. Journal of Water and Soil Science, 18 (67), 311-320, (In Persian).
Krogh, L., Breuning-Madsen, H. and Greve, M. H. (2000). Cation exchange capacity pedotransfer function for Danish soils. Plant and Soil, 50 (1), 1-12.
Kuswandari, R. (2004). Assessment of Different Methods for Measuring the Sustainability of Forest Management. International Institute for Geo-information Science and Earth Observation. Enschede, Netherlands.
Liu, S. h., Butler, D., Brazier, R., Heathwaite, L. and Khu, S. (2007). Using genetic algorithm to calibrate a water quality model. Science of the Total Environment, 374 (2-3), 260-272.
MacDonald, K. B. (1998). Development of pedotransfer functions of southern Ontario soils Report from. Greenhouse and processing crops research center. Harrow, Ontario. No: 01686-8-0436:1-23.
Manrique, L. A., Jones, C. A. and Dyke, P. T. (1991). Predicting cation exchange capacity from soil physical and chemical properties. Soil Science Society of America Journal, 50, 787-794.
McBratney, A., Odeh, O. A., Bishop, T. F., Dunbar, M. S. and Shatar, T. M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97 (3-4), 293-327.
McBratney, A. B., Minasny, B., Cattle, S. R. and Vervoort, R. W. (2002). From pedotransfer functions to soil inference systems. Geoderma. 109 (12), 41-73.
Mirkhani, R., Shabanpour, M. and Saadat, S. (2005). Using particle size distribution and organic carbon percentage to predict the cation exchange capacity of soils of Lorestan province. Iranian Journal of Soil Research. 19 (2): 235-242, (In Persian).
Mitra, B., Scott, H. D., Dixon, J. C. and McKimmey, J. M. (1998). Applications of fuzzy logic to the prediction of soil erosion in a large watershed, Geoderma, 86 (3), 183-209.
Mohammadi, J. and Taheri, M. (2005). Estimation of pedotransfer function using fuzzy regression. Journal of Agriculture Science and Technology, 2, 51-60, (In Persian).
Phillis, Y. A. and Andriantiatsaholiniaina L. A. (2001). Sustainability: an ill-defined concept and its assessment using fuzzy logic. Ecological Economics, 37(3), 435-456.
Torbert, H. A., Krueger, E. and Kurtener, D. (2008). Soil quality assessment using fuzzy modeling. International Agrophysics, 22 (4), 365-370.
Tung, C., Hsu, S., Liu, C. M. and Li, J. (2003). Application of the genetic algorithm for optimizing operation rules of the LiYuTan reservoir in Taiwan. JAWRA Journal of the American Water Resources Association, 39 (3), 649–657.
Wosten, J. H. M., Pachepsky, Y. A. and Rawls, W. J. (2001). Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology. 251 (3), 123-150.
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