TY - JOUR ID - 61691 TI - Application of CANFIS Model in Prediction of Soil Cation Exchange Capacity in Some Arid and Semi-Arid Regions of Iran JO - Journal of Range and Watershed Managment JA - JRWM LA - en SN - 5044-2008 AU - Sarmadian, Fereydoon AU - Keshavarzi, Ali AD - Professor, Department of Soil Science, Faculty of Agricultural Engineering & Technology, University of Tehran. I.R. IRAN. AD - Assistant Professor, Department of Soil Science, Faculty of Agricultural Engineering & Technology, University of Tehran. I.R.IRAN. Y1 - 2016 PY - 2016 VL - 69 IS - 2 SP - 397 EP - 410 KW - soil database KW - Data Mining KW - CEC KW - easily measurable characteristics KW - CANFIS DO - 10.22059/jrwm.2016.61691 N2 - Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability of a soil to retention of minerals and pollutants. Due to low organic matter and specific mineralogy of soils in arid and semi-arid regions, measurement of CEC is time consuming and expensive. The objective of this study was to evaluate Coactive Neuro-Fuzzy Inference System (CANFIS) in prediction of CEC in soils of arid and semi-arid regions. A total of 85 soil samples from target area were selected among 440 soil sample database (available reference database) with a ratio of 1:5. Correlation test was conducted to assess the co-linearity of independent variables. Forward regression model was used to determine the most important and influential input parameters on the output results. The results indicated the reliability and high performance of the CANFIS approach in estimation of CEC using easily measurable characteristics, organic material, and satellite images. UR - https://jrwm.ut.ac.ir/article_61691.html L1 - https://jrwm.ut.ac.ir/article_61691_9c0aec2fbc6a2745d68dae0bd8442019.pdf ER -