Modeling soil cation exchange capacity using soil parameters: Assessing the heuristic models

Accurate knowledge about soil cation exchange capacity (CEC) is very important in land drainage and reclamation, groundwater pollution studies and modeling chemical characteristics of the agricultural lands. The present study aims at developing heuristic models, e.g. gene expression programming (GEP), neuro-fuzzy (NF), neural network (NN), and support vector machine (SVM) for modeling soil CEC using soil parameters. Soil characteristic data including soil physical parameters (e.g. silt, clay and sand content), organic carbon, and pH from two different sites in Iran were utilized to feed the applied heuristic models. The models were assessed through a k-fold test data set scanning procedures, so a complete scan of the possible train and test patterns was carried out at each site. Comparison of the models showed that the NF outperforms the other applied models in both studied sites. The obtained results revealed that the performance of the applied models fluctuated throughout the test stages and between two sites, so a reliable assessment of the model should consider a complete scan of the utilized data set, which will,be a good option for preventing partially valid conclusions obtained from assessing the models based on a simple data set assignment. (C) 2017 Elsevier B.V. All rights reserved.