Prediction of soil cation exchange capacity using support vector regression optimized by genetic algorithm and adaptive network-based fuzzy inference system

Document Type: Research Paper

Authors

1 Soil Science Department, University of Jiroft, Jiroft, Iran

2 Soil Science Department, University of Rafsanjan, Rafsanjan, Iran

Abstract

Soil cation exchange capacity (CEC) is a parameter that represents soil fertility. Being difficult to measure, pedotransfer functions (PTFs) can be routinely applied for prediction of CEC by soil physicochemical properties that can be easily measured. This study developed the support vector regression (SVR) combined with genetic algorithm (GA) together with the adaptive network-based fuzzy inference system (ANFIS) to predict soil CEC based on 104 soil samples collected from soil surface under four different land uses. The database was randomly split into training and testing datasets in proportion of 70:30. The results showed that both models were accurate in predicting the soil CEC; however, comparison of the performance criteria indicated that SVR results (R2=0.84, RMSE=3.21 and MAPE=7.62) was more accurate than ANFIS results (R2=0.81, RMSE=3.38 and MAPE=10.31).  The results of sensitivity analysis showed that two parameters had the highest effect on both models were soil organic matter and clay content. 

Keywords


Arias, M., C. Pérez-Novo, F. Osorio, E. López, B. Soto, 2005. Adsorption and desorption of copper and zinc in the surface layer of acid soils. Journal of Colloid and Interface Science, 288; 21-29.

Bell, M.A., H. Van Keulen, 1995. Soil pedotransfer functions for four Mexican soils. Soil Science Society of America Journal, 59; 865-871.

Besalatpour, A.A., S. Ayoubi, M.A. Hajabbasi, M.R. Mosaddeghi, R. Schulin, 2013. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena, 111; 72-79.

Boser, B.E., I. Guyon, V. Vapnik, 1992. A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learning Theory, ed. D. Haussler. 144-152. New York: ACM Press.

Boser, B.E., E. Bernhard, M. Isabelle, I. Guyon, N. Vladimir,V. Vapnik, 1992. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, 144-152: ACM.

Chung, N., M. Alexander, 2002. Effect of soil properties on bioavailability and extractability of phenanthrene and atrazine sequestered in soil. Chemosphere, 48; 109-115.

De la Rosa, D., F. Cardona, J. Almorza, 1981. Crop yield predictions based on properties of soils in Sevilla, Spain. Geoderma, 25; 267-274.

Delle Site, A., 2001. Factors affecting sorption of organic compounds in natural sorbent/water systems and sorption coefficients for selected pollutants, A review. Journal of Physical and Chemical Reference Data, 30; 187-439.

Dibike, Y.B., V. Slavco, D. Solomatine, B.A. Abbott, 2001. Model induction with  support vector machines: introduction and applications. Journal of Computing in Civil  Engineering, 15; 208-216.

Emamgolizadeh, S., S.M. Bateni, D. Shahsavani, T. Ashrafi, H. Ghorbani, 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). Journal of Hydrology,
     529; 1590-1600.

Ersahin, S., H. Gunal, T. Kutlu, B. Yetgin, S. Coban, 2006. Estimating specific surface area and cation exchange capacity in soils using fractal dimension of particle-size distribution. Geoderma, 136; 588-597.

Evans, LJ., 1989. Chemistry of metal retention by soils. Environmental Science & Technology, 23; 1046-1056.

Gee, G.W., J.W. Bauder, 1986. Particle Size Analysis. In: Methods of Soil Analysis, Part A. Klute (ed.). 2 Ed., Vol. 9 nd . Am. Soc. Agron., Madison, WI, pp: 383-411.

Ghorbani, H., H. Kashi, N. Hafezi Moghadas, S. Emamgholizadeh, 2015. Estimation of Soil Cation Exchange Capacity using Multiple Regression, Artificial Neural Networks, and Adaptive Neuro-fuzzy Inference System Models in Golestan Province,
     Iran. Communications in Soil Science and Plant Analysis, 46; 763-780.

Horn, A.L., D. Rolf‐Alexander, G. Stefan, 2005. Comparison of the prediction efficiency of two pedotransfer functions for soil cation‐exchange capacity. Journal of Plant Nutrition and Soil Science, 168; 372-374.

Jang, J., 1993. ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics. IEEE Transactions, 23; 665-685.

Kalkhajeh, Y.K., R. Rezaie Arshad, H. Amerikhah, M. Sami. 2012, Comparison of multiple linear regressions and artificial intelligence-based modeling techniques for prediction the soil cation exchange capacity of Aridisols and Entisols in a semi-arid region. Australian Journal of Agricultural Engineering, 3; 39-40..

Kashi, H., S. Emamgholizadeh, H. Ghorbani, 2014. Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS models. Communications in Soil Science and Plant Analysis, 45; 1195-1213.

Klute, A., 1986. Methods of soil analysis. Part 1. American Society of Agronomy, Inc. Soil Science Society of America, Madison, Wisconsin, USA.   

Krogh, L., B.M. Henrik, H.G. Mogens, 2000. Cation-exchange capacity pedotransfer functions for Danish soils. Acta  Agriculturae Scandinavica, Section B-Plant Soil Science, 50; 1-12.

Lamorski, K., P. Yakov, , C. Sławiński, R.T. Walczak, 2008. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Science Society of America Journal, 72; 1243-1247.

Liao, K., X. Shaohui, W. Jichun, L. Qing Zhu, 2014. Using support vector machines to  predict cation exchange capacity of different soil horizons in Qingdao City, China. Journal of Plant  Nutrition and Soil Science, 177; 775-782.

Liong, S.Y., S. Chandrasekaran, 2002.  Flood stage forecasting with support vector  machines1, ed.: Wiley Online Library.

Madeira, M., E. Auxtero, E. Sousa, 2003. Cation and anion exchange properties of Andisols from the Azores, Portugal, as determined by the compulsive exchange and the ammonium acetate methods. Geoderma, 117; 225-241.

Michalewicz, Z., 1994. GAs: What are they? In Genetic algorithms+ data structures= evolution programs, ed. 13-30: Springer.

Nelson, D.W., L.E. Sommers, 1982. Total carbon, organic carbon, and organic matter. Methods of soil analysis. Part 2. Chemical and microbiological properties (methodsofsoilan2); 539-579.

Oorts, K., B. Vanlauwe, R. Merckx, 2003. Cation exchange capacities of soil organic matter fractions in a Ferric Lixisol with different organic matter inputs. Agriculture, Ecosystems & Environment, 100 ; 161-171.

Rhoades, J.D., 1996. Salinity: Electrical Conductivity and Total Dissolved Solids. In: Sparks, D.L., Page, A.L., Helmke, P.A., Loeppert, R.H., Soltanpour, P.N., Tabatabai, M.A., Johnston, C.T. and Sumner, M.E., Eds., Methods of Soil Analysis Part 3, Soil Science Society of America and American Society of Agronomy, Madison, pp: 417-435.

Sahrawat, K.L., 1983. An analysis of the contribution of organic matter and clay to cation exchange capacity of some Philippine soils. Communications in Soil Science & Plant Analysis, 14; 803-809.

Seybold, C.A., R.B. Grossman, T.G. Reinsch, 2005. Predicting cation exchange capacity for soil survey using linear models. Soil Science Society of America Journal, 69; 856-863.

Sharma, V.k., R.R. Daran, S. Irmak, 2013. Development and evaluation of ordinary least squares regression models for predicting irrigated and rainfed maize and soybean yields. Transactions of the ASABE, 56; 1361-1378.

Shekofteh, H., M. Afyuni, M.A. Hajabbasi, B.V. Iversen, H. Nezamabadi-pour, F. Abassi, F. Sheikholeslam, 2013. Nitrate leaching from a potato field using adaptive network-based fuzzy inference system. Journal of Hydroinformatics, 15; 503-515.

Shia, J., D. Wangxiu, L. Yanxi, 2012. Early Warning Model of Business Group Financial Risks Based on SVM. J. Inform. Comput. Sci., 9; 3813-3820.

Smola, A., J., S. Bernhard, 1998. On a kernel-based method for pattern recognition, regression, approximation, and operator inversion. Algorithmica, 22; 211-231.

Sposito, G., 2008. The chemistry of soils: Oxford university press.

Stockle, C., O. Steve A. Martin, S. Campbell, 1994. CropSyst, a cropping systems  simulation model: water/nitrogen budgets and crop yield. Agricultural Systems, 46; 335-359.

Sugeno, M., G.T. Kang, 1988. Structure identification of fuzzy model. Fuzzy sets and systems, 28; 15-33.

Syers, J.K., A.S. Campbell, T.W. Walker, 1970. Contribution of organic carbon and clay to cation exchange capacity in a chronosequence of sandy soils. Plant and Soil, 33; 104-112.

Takagi, T., M. Sugeno, 1985. Fuzzy  identification of systems and its applications to  modeling and control. Systems, Man and Cybernetics,  IEEE Transactions on, 15; 116-132.

Tang, L., Z. Guongming, F. Nourbakhsh, G. L. Shen, 2009. Artificial neural network approach for predicting cation exchange capacity in  soil based on physico-chemical properties.  Environmental Engineering Science, 26; 137-146.

Thomas, G.W., In: (Ed.), M, pp. 159-165. 1982. Exchangeable cations. In ethods of Soil Analysis, Part 2, monograph 9, ed. A. L. Page. 159-165. Madison, WI: ASA.

Twarakavi, N.K.C., J. Šimůnek, M.G. Schaap, 2009. Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machines. Soil Science Society of America Journal, 73; 1443-1452.

Vapnik, V., 1995. The Nature of Statistical Learning Theory. New York, USA: Springer.

Vapnik, V., 1998. Statistical learning theory, 1998, ed.^eds.: Wiley, New York. 2013. The nature of statistical learning theory: Springer Science & Business Media.

Visconti, F., J.M. De Paz, JL. Rubio, 2012. Choice of selectivity coefficients for cation exchange using principal components analysis and bootstrap anova of coefficients of variation. European Journal of Soil Science, 63; 501-513.

Williams, J.R., C.A. Jones, J.R. Kiniry, D.A. Spanel, 1989. The EPIC crop growth model. Transactions of the ASAE, 32; 497-0511.

Wohlberg, B., D.M. Tartakovsky, A.  Guadagnini, 2006. Subsurface characterization with  support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 44; 47-57.

Wösten, J.H.M., Y.A. Pachepsky, W.J. Rawls, 2001. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251; 123-150.

Zolfaghari, A., A.R. Taghizadeh-Mehrjardi, AR. Moshki, B.P. Malone, AO. Weldeyohannes, F. Sarmadian, M.R. Yazdani, 2016. Using the nonparametric k nearest neighbor approach for predicting cation exchange capacity. Geoderma, 265;  111-119.